Even though the Taylor-Ulitovsky process for producing microwire has existed and been widely used in the past century, there are various challenges facing the microwire manufacturing process, such as inconsistent wire diameter, constant breaks of microwire during fabrication, and the difficulty of producing wires with a smaller diameter. These challenges can make the microwire fabri- cation process inefficient, and this research aims to understand how thermal im- ages from the fabrication process under various parameter settings can be used to assess and classify the quality of the microwire. Thermal videos and other process variables were collected from a microwire manufacturing lab, and the thermal image datasets from the video were trained using a pretrained Convolutional Neural Networks (CNN) in order to better understand how changing certain pa- rameters for the microwire manufacturing process can affect the microwire qual- ity. The features extracted from the thermal images using the proposed CNN- based machine learning algorithm is capable of classifying the microwire fabri- cation process into four stages, i.e., initialization stage, stable stage 1, stable stage 2, and ending stage. The stage classification accuracy reveals high repeatability and performance from the proposed CNN model. The results are promising since manufacturing process parameter settings can be adjusted and optimized by re- ferring to thermal image characteristics, and therefore the CNN model can im- prove microwire quality and predict failure of the microwire.