diff --git a/source/application/main/include/BaseProcessing.hpp b/source/application/main/include/BaseProcessing.hpp
new file mode 100644
index 0000000..c1c3255
--- /dev/null
+++ b/source/application/main/include/BaseProcessing.hpp
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2022 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef BASE_PROCESSING_HPP
+#define BASE_PROCESSING_HPP
+
+#include "Model.hpp"
+
+namespace arm {
+namespace app {
+
+    /**
+     * @brief   Base class exposing pre-processing API.
+     *          Use cases should provide their own PreProcessing class that inherits from this one.
+     *          All steps required to take raw input data and populate tensors ready for inference
+     *          should be handled.
+     */
+    class BasePreProcess {
+
+    public:
+        virtual ~BasePreProcess() = default;
+
+        /**
+         * @brief       Should perform pre-processing of 'raw' input data and load it into
+         *              TFLite Micro input tensors ready for inference
+         * @param[in]   input      Pointer to the data that pre-processing will work on.
+         * @param[in]   inputSize  Size of the input data.
+         * @return      true if successful, false otherwise.
+         **/
+        virtual bool DoPreProcess(const void* input, size_t inputSize) = 0;
+
+    protected:
+        Model* m_model = nullptr;
+    };
+
+    /**
+     * @brief   Base class exposing post-processing API.
+     *          Use cases should provide their own PostProcessing class that inherits from this one.
+     *          All steps required to take inference output and populate results vectors should be handled.
+     */
+    class BasePostProcess {
+
+    public:
+        virtual ~BasePostProcess() = default;
+
+        /**
+         * @brief       Should perform post-processing of the result of inference then populate
+         *              populate result data for any later use.
+         * @return      true if successful, false otherwise.
+         **/
+        virtual bool DoPostProcess() = 0;
+
+    protected:
+        Model* m_model = nullptr;
+    };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* BASE_PROCESSING_HPP */
\ No newline at end of file
diff --git a/source/application/main/include/UseCaseCommonUtils.hpp b/source/application/main/include/UseCaseCommonUtils.hpp
index 9b6d550..f79f6ed 100644
--- a/source/application/main/include/UseCaseCommonUtils.hpp
+++ b/source/application/main/include/UseCaseCommonUtils.hpp
@@ -24,6 +24,7 @@
 #include "UseCaseHandler.hpp"       /* Handlers for different user options. */
 #include "Classifier.hpp"           /* Classifier. */
 #include "InputFiles.hpp"
+#include "BaseProcessing.hpp"
 
 
 void DisplayCommonMenu();
@@ -107,6 +108,67 @@
      **/
     bool ListFilesHandler(ApplicationContext& ctx);
 
+    /**
+     * @brief   Use case runner class that will handle calling pre-processing,
+     *          inference and post-processing.
+     *          After constructing an instance of this class the user can call
+     *          PreProcess(), RunInference() and PostProcess() to perform inference.
+     */
+    class UseCaseRunner {
+
+    private:
+        BasePreProcess* m_preProcess;
+        BasePostProcess* m_postProcess;
+        Model* m_model;
+
+    public:
+        explicit UseCaseRunner(BasePreProcess* preprocess, BasePostProcess* postprocess, Model* model)
+        : m_preProcess{preprocess},
+          m_postProcess{postprocess},
+          m_model{model}
+          {};
+
+        /**
+         * @brief       Runs pre-processing as defined by PreProcess object within the runner.
+         *              Templated for the input data type.
+         * @param[in]   inputData    Pointer to the data that inference will be performed on.
+         * @param[in]   inputSize    Size of the input data that inference will be performed on.
+         * @return      true if successful, false otherwise.
+         **/
+        template<typename T>
+        bool PreProcess(T* inputData, size_t inputSize) {
+            if (!this->m_preProcess->DoPreProcess(inputData, inputSize)) {
+                printf_err("Pre-processing failed.");
+                return false;
+            }
+            return true;
+        }
+
+        /**
+         * @brief       Runs inference with the Model object within the runner.
+         * @return      true if successful, false otherwise.
+         **/
+        bool RunInference() {
+            if (!this->m_model->RunInference()) {
+                printf_err("Inference failed.");
+                return false;
+            }
+            return true;
+        }
+
+        /**
+         * @brief       Runs post-processing as defined by PostProcess object within the runner.
+         * @return      true if successful, false otherwise.
+         **/
+        bool PostProcess() {
+            if (!this->m_postProcess->DoPostProcess()) {
+                printf_err("Post-processing failed.");
+                return false;
+            }
+            return true;
+        }
+    };
+
 } /* namespace app */
 } /* namespace arm */
 
diff --git a/source/use_case/img_class/include/ImgClassProcessing.hpp b/source/use_case/img_class/include/ImgClassProcessing.hpp
new file mode 100644
index 0000000..5a59b5f
--- /dev/null
+++ b/source/use_case/img_class/include/ImgClassProcessing.hpp
@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2022 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef IMG_CLASS_PROCESSING_HPP
+#define IMG_CLASS_PROCESSING_HPP
+
+#include "BaseProcessing.hpp"
+#include "Model.hpp"
+#include "Classifier.hpp"
+
+namespace arm {
+namespace app {
+
+    /**
+     * @brief   Pre-processing class for Image Classification use case.
+     *          Implements methods declared by BasePreProcess and anything else needed
+     *          to populate input tensors ready for inference.
+     */
+    class ImgClassPreProcess : public BasePreProcess {
+
+    public:
+        explicit ImgClassPreProcess(Model* model);
+
+        bool DoPreProcess(const void* input, size_t inputSize) override;
+    };
+
+    /**
+     * @brief   Post-processing class for Image Classification use case.
+     *          Implements methods declared by BasePostProcess and anything else needed
+     *          to populate result vector.
+     */
+    class ImgClassPostProcess : public BasePostProcess {
+
+    private:
+        Classifier& m_imgClassifier;
+        const std::vector<std::string>& m_labels;
+        std::vector<ClassificationResult>& m_results;
+
+    public:
+        ImgClassPostProcess(Classifier& classifier, Model* model,
+                            const std::vector<std::string>& labels,
+                            std::vector<ClassificationResult>& results);
+
+        bool DoPostProcess() override;
+    };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* IMG_CLASS_PROCESSING_HPP */
\ No newline at end of file
diff --git a/source/use_case/img_class/src/ImgClassProcessing.cc b/source/use_case/img_class/src/ImgClassProcessing.cc
new file mode 100644
index 0000000..e33e3c1
--- /dev/null
+++ b/source/use_case/img_class/src/ImgClassProcessing.cc
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2022 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "ImgClassProcessing.hpp"
+#include "ImageUtils.hpp"
+#include "log_macros.h"
+
+namespace arm {
+namespace app {
+
+    ImgClassPreProcess::ImgClassPreProcess(Model* model)
+    {
+        this->m_model = model;
+    }
+
+    bool ImgClassPreProcess::DoPreProcess(const void* data, size_t inputSize)
+    {
+        if (data == nullptr) {
+            printf_err("Data pointer is null");
+        }
+
+        auto input = static_cast<const uint8_t*>(data);
+        TfLiteTensor* inputTensor = this->m_model->GetInputTensor(0);
+
+        memcpy(inputTensor->data.data, input, inputSize);
+        debug("Input tensor populated \n");
+
+        if (this->m_model->IsDataSigned()) {
+            image::ConvertImgToInt8(inputTensor->data.data, inputTensor->bytes);
+        }
+
+        return true;
+    }
+
+    ImgClassPostProcess::ImgClassPostProcess(Classifier& classifier, Model* model,
+                                             const std::vector<std::string>& labels,
+                                             std::vector<ClassificationResult>& results)
+            :m_imgClassifier{classifier},
+             m_labels{labels},
+             m_results{results}
+    {
+        this->m_model = model;
+    }
+
+    bool ImgClassPostProcess::DoPostProcess()
+    {
+        return this->m_imgClassifier.GetClassificationResults(
+                this->m_model->GetOutputTensor(0), this->m_results,
+                this->m_labels, 5, false);
+    }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index 9061282..98e2b59 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/src/UseCaseHandler.cc
@@ -23,6 +23,7 @@
 #include "UseCaseCommonUtils.hpp"
 #include "hal.h"
 #include "log_macros.h"
+#include "ImgClassProcessing.hpp"
 
 #include <cinttypes>
 
@@ -31,20 +32,12 @@
 namespace arm {
 namespace app {
 
-    /**
-    * @brief           Helper function to load the current image into the input
-    *                  tensor.
-    * @param[in]       imIdx         Image index (from the pool of images available
-    *                                to the application).
-    * @param[out]      inputTensor   Pointer to the input tensor to be populated.
-    * @return          true if tensor is loaded, false otherwise.
-    **/
-    static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor);
-
-    /* Image inference classification handler. */
+    /* Image classification inference handler. */
     bool ClassifyImageHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
     {
         auto& profiler = ctx.Get<Profiler&>("profiler");
+        auto& model = ctx.Get<Model&>("model");
+        auto initialImIdx = ctx.Get<uint32_t>("imgIndex");
 
         constexpr uint32_t dataPsnImgDownscaleFactor = 2;
         constexpr uint32_t dataPsnImgStartX = 10;
@@ -53,8 +46,6 @@
         constexpr uint32_t dataPsnTxtInfStartX = 150;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
 
-        auto& model = ctx.Get<Model&>("model");
-
         /* If the request has a valid size, set the image index. */
         if (imgIndex < NUMBER_OF_FILES) {
             if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
@@ -66,11 +57,7 @@
             return false;
         }
 
-        auto curImIdx = ctx.Get<uint32_t>("imgIndex");
-
-        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
-
         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
@@ -79,13 +66,20 @@
             return false;
         }
 
+        /* Get input shape for displaying the image. */
         TfLiteIntArray* inputShape = model.GetInputShape(0);
-
         const uint32_t nCols = inputShape->data[arm::app::MobileNetModel::ms_inputColsIdx];
         const uint32_t nRows = inputShape->data[arm::app::MobileNetModel::ms_inputRowsIdx];
         const uint32_t nChannels = inputShape->data[arm::app::MobileNetModel::ms_inputChannelsIdx];
 
+        /* Set up pre and post-processing. */
+        ImgClassPreProcess preprocess = ImgClassPreProcess(&model);
+
         std::vector<ClassificationResult> results;
+        ImgClassPostProcess postprocess = ImgClassPostProcess(ctx.Get<ImgClassClassifier&>("classifier"), &model,
+                ctx.Get<std::vector<std::string>&>("labels"), results);
+
+        UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
 
         do {
             hal_lcd_clear(COLOR_BLACK);
@@ -93,29 +87,42 @@
             /* Strings for presentation/logging. */
             std::string str_inf{"Running inference... "};
 
-            /* Copy over the data. */
-            LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor);
+            const uint8_t* imgSrc = get_img_array(ctx.Get<uint32_t>("imgIndex"));
+            if (nullptr == imgSrc) {
+                printf_err("Failed to get image index %" PRIu32 " (max: %u)\n", ctx.Get<uint32_t>("imgIndex"),
+                           NUMBER_OF_FILES - 1);
+                return false;
+            }
 
             /* Display this image on the LCD. */
             hal_lcd_display_image(
-                static_cast<uint8_t *>(inputTensor->data.data),
+                imgSrc,
                 nCols, nRows, nChannels,
                 dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
 
-            /* If the data is signed. */
-            if (model.IsDataSigned()) {
-                image::ConvertImgToInt8(inputTensor->data.data, inputTensor->bytes);
-            }
-
             /* Display message on the LCD - inference running. */
             hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
                                     dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
-            /* Run inference over this image. */
+            /* Select the image to run inference with. */
             info("Running inference on image %" PRIu32 " => %s\n", ctx.Get<uint32_t>("imgIndex"),
                 get_filename(ctx.Get<uint32_t>("imgIndex")));
 
-            if (!RunInference(model, profiler)) {
+            const size_t imgSz = inputTensor->bytes < IMAGE_DATA_SIZE ?
+                                  inputTensor->bytes : IMAGE_DATA_SIZE;
+
+            /* Run the pre-processing, inference and post-processing. */
+            if (!runner.PreProcess(imgSrc, imgSz)) {
+                return false;
+            }
+
+            profiler.StartProfiling("Inference");
+            if (!runner.RunInference()) {
+                return false;
+            }
+            profiler.StopProfiling();
+
+            if (!runner.PostProcess()) {
                 return false;
             }
 
@@ -124,15 +131,11 @@
             hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
                                     dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
-            auto& classifier = ctx.Get<ImgClassClassifier&>("classifier");
-            classifier.GetClassificationResults(outputTensor, results,
-                                                ctx.Get<std::vector <std::string>&>("labels"),
-                                                5, false);
-
             /* Add results to context for access outside handler. */
             ctx.Set<std::vector<ClassificationResult>>("results", results);
 
 #if VERIFY_TEST_OUTPUT
+            TfLiteTensor* outputTensor = model.GetOutputTensor(0);
             arm::app::DumpTensor(outputTensor);
 #endif /* VERIFY_TEST_OUTPUT */
 
@@ -144,27 +147,10 @@
 
             IncrementAppCtxIfmIdx(ctx,"imgIndex");
 
-        } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx);
+        } while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImIdx);
 
         return true;
     }
 
-    static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor)
-    {
-        const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ?
-                              inputTensor->bytes : IMAGE_DATA_SIZE;
-        const uint8_t* imgSrc = get_img_array(imIdx);
-        if (nullptr == imgSrc) {
-            printf_err("Failed to get image index %" PRIu32 " (max: %u)\n", imIdx,
-                       NUMBER_OF_FILES - 1);
-            return false;
-        }
-
-        memcpy(inputTensor->data.data, imgSrc, copySz);
-        debug("Image %" PRIu32 " loaded\n", imIdx);
-        return true;
-    }
-
-
 } /* namespace app */
 } /* namespace arm */