Multi-touch technology provides a successful gesture based human computer interface. The contact and gesture recognition algorithms of this interface are based on full hand function and, therefore, are not accessible to many people with physical disability. In this paper, we design a set of command-like gestures for users with limited range and function in their digits and wrist. Trajectory and angle features are extracted from these gestures and passed to a recurrent neural network for recognition. Experiments are performed to test the feasibility of the gesture recognition system and determine the effect of network topology on the gesture recognition rate. These results show that the proposed method can successfully recognize those designed gestures for disabilities.