diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
new file mode 100644
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+++ b/source/use_case/img_class/src/UseCaseHandler.cc
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+/*
+ * Copyright (c) 2021 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 "UseCaseHandler.hpp"
+
+#include "Classifier.hpp"
+#include "InputFiles.hpp"
+#include "MobileNetModel.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "hal.h"
+
+using ImgClassClassifier = arm::app::Classifier;
+
+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);
+
+    /**
+     * @brief           Helper function to increment current image index.
+     * @param[in,out]   ctx   Pointer to the application context object.
+     **/
+    static void _IncrementAppCtxImageIdx(ApplicationContext& ctx);
+
+    /**
+     * @brief           Helper function to set the image index.
+     * @param[in,out]   ctx   Pointer to the application context object.
+     * @param[in]       idx   Value to be set.
+     * @return          true if index is set, false otherwise.
+     **/
+    static bool _SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx);
+
+    /**
+     * @brief           Presents inference results using the data presentation
+     *                  object.
+     * @param[in]       platform    Reference to the hal platform object.
+     * @param[in]       results     Vector of classification results to be displayed.
+     * @param[in]       infTimeMs   Inference time in milliseconds, if available
+     *                              otherwise, this can be passed in as 0.
+     * @return          true if successful, false otherwise.
+     **/
+    static bool _PresentInferenceResult(hal_platform& platform,
+                                        const std::vector<ClassificationResult>& results);
+
+    /**
+     * @brief           Helper function to convert a UINT8 image to INT8 format.
+     * @param[in,out]   data            Pointer to the data start.
+     * @param[in]       kMaxImageSize   Total number of pixels in the image.
+     **/
+    static void ConvertImgToInt8(void* data, size_t kMaxImageSize);
+
+    /* Image inference classification handler. */
+    bool ClassifyImageHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
+    {
+        auto& platform = ctx.Get<hal_platform&>("platform");
+
+        constexpr uint32_t dataPsnImgDownscaleFactor = 2;
+        constexpr uint32_t dataPsnImgStartX = 10;
+        constexpr uint32_t dataPsnImgStartY = 35;
+
+        constexpr uint32_t dataPsnTxtInfStartX = 150;
+        constexpr uint32_t dataPsnTxtInfStartY = 40;
+
+        platform.data_psn->clear(COLOR_BLACK);
+
+        auto& model = ctx.Get<Model&>("model");
+
+        /* If the request has a valid size, set the image index. */
+        if (imgIndex < NUMBER_OF_FILES) {
+            if (!_SetAppCtxImageIdx(ctx, imgIndex)) {
+                return false;
+            }
+        }
+        if (!model.IsInited()) {
+            printf_err("Model is not initialised! Terminating processing.\n");
+            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;
+        } else if (inputTensor->dims->size < 3) {
+            printf_err("Input tensor dimension should be >= 3\n");
+            return false;
+        }
+
+        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];
+
+        std::vector<ClassificationResult> results;
+
+        do {
+            /* Strings for presentation/logging. */
+            std::string str_inf{"Running inference... "};
+
+            /* Copy over the data. */
+            _LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor);
+
+            /* Display this image on the LCD. */
+            platform.data_psn->present_data_image(
+                (uint8_t*) inputTensor->data.data,
+                nCols, nRows, nChannels,
+                dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
+
+            /* If the data is signed. */
+            if (model.IsDataSigned()) {
+                ConvertImgToInt8(inputTensor->data.data, inputTensor->bytes);
+            }
+
+            /* Display message on the LCD - inference running. */
+            platform.data_psn->present_data_text(str_inf.c_str(), str_inf.size(),
+                                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+
+            /* Run inference over this image. */
+            info("Running inference on image %u => %s\n", ctx.Get<uint32_t>("imgIndex"),
+                get_filename(ctx.Get<uint32_t>("imgIndex")));
+
+            RunInference(platform, model);
+
+            /* Erase. */
+            str_inf = std::string(str_inf.size(), ' ');
+            platform.data_psn->present_data_text(str_inf.c_str(), str_inf.size(),
+                                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+
+            auto& classifier = ctx.Get<ImgClassClassifier&>("classifier");
+            classifier.GetClassificationResults(outputTensor, results,
+                                                ctx.Get<std::vector <std::string>&>("labels"),
+                                                5);
+
+            /* Add results to context for access outside handler. */
+            ctx.Set<std::vector<ClassificationResult>>("results", results);
+
+#if VERIFY_TEST_OUTPUT
+            arm::app::DumpTensor(outputTensor);
+#endif /* VERIFY_TEST_OUTPUT */
+
+            if (!_PresentInferenceResult(platform, results)) {
+                return false;
+            }
+
+            _IncrementAppCtxImageIdx(ctx);
+
+        } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx);
+
+        return true;
+    }
+
+    static bool _LoadImageIntoTensor(const 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 %u (max: %u)\n", imIdx,
+                       NUMBER_OF_FILES - 1);
+            return false;
+        }
+
+        memcpy(inputTensor->data.data, imgSrc, copySz);
+        debug("Image %u loaded\n", imIdx);
+        return true;
+    }
+
+    static void _IncrementAppCtxImageIdx(ApplicationContext& ctx)
+    {
+        auto curImIdx = ctx.Get<uint32_t>("imgIndex");
+
+        if (curImIdx + 1 >= NUMBER_OF_FILES) {
+            ctx.Set<uint32_t>("imgIndex", 0);
+            return;
+        }
+        ++curImIdx;
+        ctx.Set<uint32_t>("imgIndex", curImIdx);
+    }
+
+    static bool _SetAppCtxImageIdx(ApplicationContext& ctx, const uint32_t idx)
+    {
+        if (idx >= NUMBER_OF_FILES) {
+            printf_err("Invalid idx %u (expected less than %u)\n",
+                       idx, NUMBER_OF_FILES);
+            return false;
+        }
+        ctx.Set<uint32_t>("imgIndex", idx);
+        return true;
+    }
+
+    static bool _PresentInferenceResult(hal_platform& platform,
+                                        const std::vector<ClassificationResult>& results)
+    {
+        constexpr uint32_t dataPsnTxtStartX1 = 150;
+        constexpr uint32_t dataPsnTxtStartY1 = 30;
+
+        constexpr uint32_t dataPsnTxtStartX2 = 10;
+        constexpr uint32_t dataPsnTxtStartY2 = 150;
+
+        constexpr uint32_t dataPsnTxtYIncr = 16;  /* Row index increment. */
+
+        platform.data_psn->set_text_color(COLOR_GREEN);
+
+        /* Display each result. */
+        uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
+        uint32_t rowIdx2 = dataPsnTxtStartY2;
+
+        for (uint32_t i = 0; i < results.size(); ++i) {
+            std::string resultStr =
+                std::to_string(i + 1) + ") " +
+                std::to_string(results[i].m_labelIdx) +
+                " (" + std::to_string(results[i].m_normalisedVal) + ")";
+
+            platform.data_psn->present_data_text(
+                                        resultStr.c_str(), resultStr.size(),
+                                        dataPsnTxtStartX1, rowIdx1, 0);
+            rowIdx1 += dataPsnTxtYIncr;
+
+            resultStr = std::to_string(i + 1) + ") " + results[i].m_label;
+            platform.data_psn->present_data_text(
+                                        resultStr.c_str(), resultStr.size(),
+                                        dataPsnTxtStartX2, rowIdx2, 0);
+            rowIdx2 += dataPsnTxtYIncr;
+
+            info("%u) %u (%f) -> %s\n", i, results[i].m_labelIdx,
+                 results[i].m_normalisedVal, results[i].m_label.c_str());
+        }
+
+        return true;
+    }
+
+    static void ConvertImgToInt8(void* data, const size_t kMaxImageSize)
+    {
+        auto* tmp_req_data = (uint8_t*) data;
+        auto* tmp_signed_req_data = (int8_t*) data;
+
+        for (size_t i = 0; i < kMaxImageSize; i++) {
+            tmp_signed_req_data[i] = (int8_t) (
+                (int32_t) (tmp_req_data[i]) - 128);
+        }
+    }
+
+} /* namespace app */
+} /* namespace arm */