Surface electromyogram (sEMG) has numerous applications. It has been widely used in various biosignal and neuro rehabilitation 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. Earlier work to identify the hand actions and gestures based on sEMG suffers from limitation that these are suitable for gross actions where there is only one prime-mover muscle involved and not suitable for small subtle and complex muscle contraction. This paper presents the hand gesture identification using sEMG decomposed using semi-blind independent component analysis combined with neural network based classifier. The aim was to provide reliable and natural control for rehabilitation and human computer interaction applications. We have proposed a model based approach where the hand muscle anatomy is known. The system was tested on 5 subjects and with experiments repeated on different days. The system was compared with raw sEMG as used by other researchers. The system is able to classify the different hand actions 100%. In comparison, the classification of the traditional ICA and raw sEMG for the same experiments and similar features was a poor 65% and 60% respectively. This research demonstrates that sEMG can be decomposed to the individual muscle activities using semi-blind ICA. The muscle activity after decomposition can be used to accurately identify small and subtle hand actions and gestures. Finally the ICA source separation was validated with mixing matrix analysis.