This study explores several machine learning methodologies to effectively classify distinct hand gestures using data collected by a gauntlet embedded with multiple surface electromyography (sEMG) sensors placed to capture muscle activation patterns. The analysis includes correlations between sensor data, filtering techniques, and a variety of machine learning algorithms. These efforts had the goal of identifying approaches that could facilitate the development of a commercially viable device. By evaluating the classification accuracy and computational efficiency of each method during the validation phase, this study seeks to identify techniques to be used for further refinement in this kind of product.