Modern Prosthetics for the rehabilitation of hand amputees has been improved recently, but the value of prosthetics for the freedom of movement and adoption in the daily life of amputees is still substandard. The Electromyography (EMG) signals are generated by human muscle systems when there are any movements and muscular activities. These signals are detected over different areas from the skin surfaces and each movement corresponds to a specific activation pattern of several muscles. In this research, multi-channel EMG measurements were performed with electrodes placed on involved arm muscles. Since deltoid, bicep brachii, pectoris major, and flexor digitorium muscles can almost independently move human arm with adjustable contraction forces, the surface EMG signals from these muscles were utilized to recognize different arm movements. The EMG signals were digitally recorded and processed using digital filters, feature extraction methods, and classification algorithms. For feature extraction, the envelopes were extracted from the signal waveforms. To reflect the moving average activities, the root means squares (RMS) operation and normalization were successively utilized as initial signal processing method. Afterward, an activation vector containing normalized RMS signals was obtained in realtime. For machine learning, the activation vectors were utilized to train a real-time support vector machine (SVM) classifier to recognize different muscle EMG signals and their respective motion commands. A detailed analysis using SVM reveals more than 98% accuracy for recognition and successful classification of different motion commands after training. The effectiveness of the proposed method was verified through several experiments.