This document reviews research on using electromyography (EMG) signals to control a prosthetic hand with multiple movements. EMG signals are acquired from forearm muscles and analyzed using wavelet transforms and artificial neural networks to classify hand movements like wrist extension, hand opening/closing, and thumb movements. The goal is to develop a prosthetic hand that can perform dexterous grasping movements in a natural way by sensitively responding to the user's intended movements. Challenges include noise reduction in EMG signals and classifying movements within the 100ms timeframe needed for real-time control of a prosthetic hand.