An adaptive neural network control scheme is presented for a class of SISO affine nonlinear systems. Parameters in neural networks are updated using a gradient descent method. No robustifying control term is used in controller. The convergence of adaptive parameters and tracking error and the boundedness of all states in the corresponding closed-loop system are demonstrated by Lyapunov stability theorem. Simulation results demonstrate the effectiveness of the approach.