Abstract A study of electromyographic signals (EMG) in subjects with partial hand amputation using machine learning techniques (ML) is presented in this document. The EMG were analyzed for five hand poses. We used the Fast Fourier Transform (FFT), and Wavelet transform as descriptors for the feature extraction, the identification and classification system was implemented based on Vector Support Machines (VSM). Percentages of accuracy greater than 90% were obtained in the cases of close hand, left hand, right hand and relax hand, while open hand obtained an acceptable performance with accuracy percentages lower than 90%.