There are an increasing number of disabled people in the world. These people face many problems going about their day to day lives, in order to improve the day to day lives of these people, it is important to give much attention to the research of artificial lower and upper limb prostheses Conventionally, different pattern recognition and learning networks must be developed for EMG signals extracted from different people, but an exceptional method for pattern classification utilizing EMG signals from forearm muscles of the upper limb is introduced in this paper. This method allows the use of one network for different people without dropping the accuracy, overcoming the problem of individual difference during EMG signal collection. This can be achieved in 2 different ways. The first way, 6 different time domain feature extraction methods are combined using a regular pattern attaining 22 new features which are used with 6 different main classifiers with a total of 22 sub classifiers. This is done to identify which classifier gives the highest classification accuracy. In the second method, combining the feature extraction method using the sequence (X, XY, Y) provides high accuracy and makes it possible to use one network for classifying different people hand gesture without any drop in the accuracy.