diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc
new file mode 100644
index 0000000..bc2c378
--- /dev/null
+++ b/source/application/main/Classifier.cc
@@ -0,0 +1,191 @@
+/*
+ * 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 "Classifier.hpp"
+
+#include "hal.h"
+#include "TensorFlowLiteMicro.hpp"
+
+#include <vector>
+#include <string>
+#include <set>
+#include <cstdint>
+
+namespace arm {
+namespace app {
+
+    template<typename T>
+    bool Classifier::_GetTopNResults(TfLiteTensor* tensor,
+                         std::vector<ClassificationResult>& vecResults,
+                         uint32_t topNCount,
+                         const std::vector <std::string>& labels)
+    {
+        std::set<std::pair<T, uint32_t>> sortedSet;
+
+        /* NOTE: inputVec's size verification against labels should be
+         *       checked by the calling/public function. */
+        T* tensorData = tflite::GetTensorData<T>(tensor);
+
+        /* Set initial elements. */
+        for (uint32_t i = 0; i < topNCount; ++i) {
+            sortedSet.insert({tensorData[i], i});
+        }
+
+        /* Initialise iterator. */
+        auto setFwdIter = sortedSet.begin();
+
+        /* Scan through the rest of elements with compare operations. */
+        for (uint32_t i = topNCount; i < labels.size(); ++i) {
+            if (setFwdIter->first < tensorData[i]) {
+                sortedSet.erase(*setFwdIter);
+                sortedSet.insert({tensorData[i], i});
+                setFwdIter = sortedSet.begin();
+            }
+        }
+
+        /* Final results' container. */
+        vecResults = std::vector<ClassificationResult>(topNCount);
+
+        /* For getting the floating point values, we need quantization parameters. */
+        QuantParams quantParams = GetTensorQuantParams(tensor);
+
+        /* Reset the iterator to the largest element - use reverse iterator. */
+        auto setRevIter = sortedSet.rbegin();
+
+        /* Populate results
+         * Note: we could combine this loop with the loop above, but that
+         *       would, involve more multiplications and other operations.
+         **/
+        for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
+            double score = static_cast<int> (setRevIter->first);
+            vecResults[i].m_normalisedVal = quantParams.scale *
+                                         (score - quantParams.offset);
+            vecResults[i].m_label = labels[setRevIter->second];
+            vecResults[i].m_labelIdx = setRevIter->second;
+        }
+
+        return true;
+    }
+
+    template<>
+    bool Classifier::_GetTopNResults<float>(TfLiteTensor* tensor,
+                                     std::vector<ClassificationResult>& vecResults,
+                                     uint32_t topNCount,
+                                     const std::vector <std::string>& labels)
+    {
+        std::set<std::pair<float, uint32_t>> sortedSet;
+
+        /* NOTE: inputVec's size verification against labels should be
+         *       checked by the calling/public function. */
+        float* tensorData = tflite::GetTensorData<float>(tensor);
+
+        /* Set initial elements. */
+        for (uint32_t i = 0; i < topNCount; ++i) {
+            sortedSet.insert({tensorData[i], i});
+        }
+
+        /* Initialise iterator. */
+        auto setFwdIter = sortedSet.begin();
+
+        /* Scan through the rest of elements with compare operations. */
+        for (uint32_t i = topNCount; i < labels.size(); ++i) {
+            if (setFwdIter->first < tensorData[i]) {
+                sortedSet.erase(*setFwdIter);
+                sortedSet.insert({tensorData[i], i});
+                setFwdIter = sortedSet.begin();
+            }
+        }
+
+        /* Final results' container. */
+        vecResults = std::vector<ClassificationResult>(topNCount);
+
+        /* Reset the iterator to the largest element - use reverse iterator. */
+        auto setRevIter = sortedSet.rbegin();
+
+        /* Populate results
+         * Note: we could combine this loop with the loop above, but that
+         *       would, involve more multiplications and other operations.
+         **/
+        for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
+            vecResults[i].m_normalisedVal = setRevIter->first;
+            vecResults[i].m_label = labels[setRevIter->second];
+            vecResults[i].m_labelIdx = setRevIter->second;
+        }
+
+        return true;
+    }
+
+    template bool  Classifier::_GetTopNResults<uint8_t>(TfLiteTensor* tensor,
+                                           std::vector<ClassificationResult>& vecResults,
+                                           uint32_t topNCount, const std::vector <std::string>& labels);
+
+    template bool  Classifier::_GetTopNResults<int8_t>(TfLiteTensor* tensor,
+                                          std::vector<ClassificationResult>& vecResults,
+                                          uint32_t topNCount, const std::vector <std::string>& labels);
+
+    bool  Classifier::GetClassificationResults(
+        TfLiteTensor* outputTensor,
+        std::vector<ClassificationResult>& vecResults,
+        const std::vector <std::string>& labels, uint32_t topNCount)
+    {
+        if (outputTensor == nullptr) {
+            printf_err("Output vector is null pointer.\n");
+            return false;
+        }
+
+        uint32_t totalOutputSize = 1;
+        for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++){
+            totalOutputSize *= outputTensor->dims->data[inputDim];
+        }
+
+        /* Sanity checks. */
+        if (totalOutputSize < topNCount) {
+            printf_err("Output vector is smaller than %u\n", topNCount);
+            return false;
+        } else if (totalOutputSize != labels.size()) {
+            printf_err("Output size doesn't match the labels' size\n");
+            return false;
+        }
+
+        bool resultState;
+        vecResults.clear();
+
+        /* Get the top N results. */
+        switch (outputTensor->type) {
+            case kTfLiteUInt8:
+                resultState = _GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
+                break;
+            case kTfLiteInt8:
+                resultState = _GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
+                break;
+            case kTfLiteFloat32:
+                resultState = _GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
+                break;
+            default:
+                printf_err("Tensor type %s not supported by classifier\n", TfLiteTypeGetName(outputTensor->type));
+                return false;
+        }
+
+        if (!resultState) {
+            printf_err("Failed to get sorted set\n");
+            return false;
+        }
+
+        return true;
+    }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file