Abstract: A systematic procedure for synthesis of robust adaptive neural network control is proposed for a class of strict-feedback nonlinear systems with both unknown system nonlinearities and unknown virtual control gain nonlinearities. By employing radial based function neural network (RBF NN) to approximate uncertain nonlinear system functions, and nonlinear damping item to compensate for both external disturbance and modeling error, and by combining dynamic surface control (DSC) with backstepping technique and Nussbaum gain approach, the algorithm can not only overcome both the ``explosion of complexity'' problem inherent in the backstepping method and the possible ``controller singularity'' problem, but also reduce dramatically the number of on-line learning parameters, thus reducing the computation load of the algorithm correspondingly and making it easy in actual implementation. The stability analysis shows that all closed-loop signals are semi-global uniformly ultimately bounded (SGUUB), with the tracking error converging to a small neighborhood of the origin by appropriately choosing design constants. Finally, simulation results are presented to show the effectiveness of the proposed algorithm.