A robust Artificial Neural Network controller is proposed for the motion control of a magnetic disk drive voice coil motor (voice coil motor). The neural controller is used to approximate the nonlinear functions (actuator electromechanical dynamics) of the voice coil motor while having on line training. One main advantage of this approach, when compared with standard adaptive control, is that complex dynamical analysis is not needed. Using this design, not only strong robustness with respect to uncertain dynamics and non-linearities can be obtained, but also the output tracking error between the plant output and the desired reference can asymptotically converge to zero. Additionally, standard offline training, utilizing training vectors to stimulate the voice coil motor, is not required.