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A neural sliding mode controller is given for trajectory tracking control of multi-link robots with uncertain external disturbances and system model errors. This control scheme uses global sliding surface, integrates the characteristic of sliding mode control theory and the nonlinear mapping of neural network, Radial Basis Function neural network is applied to learn the unknown bounds of system uncertainties, reduce the chattering of sliding mode controller, at the same time the system has strong robustness. Based on the Lyapunov principle, sufficient conditions for system stability are given. Simulation results verify that this method improves the performances of trajectory tracking and speed tracking, enhances the robustness to modeling error and external disturbances.