This research project explores the development and assessment of a Handwritten Number Classifier based on Neural Networks, employing the widely used MNIST dataset. The primary objective of this study is to enhance computational efficiency, and to achieve this, it concentrates on integrating and contrasting non-pipelined and pipelined structures within the Multiply-Accumulate (MAC) unit. The initial phase involved conducting MATLAB simulations, which yielded promising results regarding the accuracy of weight calculations. Subsequently, Hardware Description Language (HDL) testing was carried out to further validate the classifiers performance. In the HDL testing phase, the classifier incorporating the pipelined MAC unit demonstrated a substantial 42.9% enhancement in processing speed when compared to its non-pipelined counterpart. These results highlight the potential advantages of employing pipeline processing in neural network architectures, emphasizing its effectiveness in achieving faster and more efficient image classification, particularly when dealing with extensive datasets. In conclusion, this research project not only presents valuable insights into improving the efficiency of neural network-based image classifiers but also lays the groundwork for potential future endeavors. These future directions may include adapting the classifier to handle more complex datasets and addressing emerging challenges.