Hand gesture identification has various human computer interaction (HCI) applications. There is an urgent need for establishing a simple yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Here, an approach is explained to demonstrate how hand gestures can be identified from isometric muscular activity, where signal level is low and changes are very subtle. Obvious difficulties arise from a very poor signal to noise ratio in the recorded electromyograms (EMG). Independent component analysis (ICA) is applied to separate these low-level muscle activities. The order and magnitude ambiguity of ICA have been overcome by using a priori knowledge of the hand muscle anatomy and a fixed un-mixing matrix. The classification is achieved using a back-propagation neural network. Experimental results are shown, where the system was able to reliably recognize motionless gestures. The system was tested across users to investigate the impact of inter-subject variation. The experimental results demonstrate an overall accuracy of 96%, and the system was shown being insensitive against electrode positions, since these successful experiments were repeated on different days. The advantage of such a system is, that it is easy to train by a lay user, and that it can easily be implemented as real-time processing after an initial training. Hence, EMG-based input devices can provide an effective solution for designing mobile interfaces that are subtle and intimate, and there exist a range of applications for communication, emotive machines and human computer interface.