The aim of this paper consists in using one of the emergent techniques which proves its capability of improving performances of several systems, called "neuro-fuzzy", in order to reduce the chattering phenomenon and also to perform the control obtained with fuzzy sliding mode control. In fact, after determining the decoupled model of the motor, a set of simple surfaces and associated a smooth control function with a threshold have been synthesized. However, the magnitude of this control function depends closely on the upper bound of uncertainties, which include parameter variations and external disturbances, and this generates chattering. Usually, the upper bound of uncertainties is difficult to be known before motor operation, so a fuzzy sliding mode controller is investigated to solve this difficulty and in which a simple fuzzy inference mechanism is used to reduce the chattering phenomenon by simple adjustments. In order to optimize the control performances and ensure a significant reduction of chattering compare with ones obtained in the previous fuzzy sliding mode, we propose in this paper to use adaptive predictive neural approach to regulate the speed of the motor. The neural control algorithm is provided with the predicted system output which is the speed variable via a recursive on line identification of the overall system which is based on a static feed forward linear network with one hidden layer. The predicted data are passed to a numerical optimization algorithm which attempts to minimize a quadratic performance criterion to compute the suitable control signal.