In this paper, a stability based approach is introduced to design neural controllers for nonlinear systems. The requisite control input is generated as the output of a neural network, which is trained off-line such that the time derivative of a positive definite function of the state variables becomes negative at all points. By using the successfully trained networks as controllers, the closed-loop system can be made stable. The stability framework introduced permits the generation of more efficient algorithms that lead to a larger region of stability for a wide class of nonlinear systems.