An innovative variable-structure-systems-based approach for online training of neural network (NN) controllers as applied to the speed control of electric drives is presented. The proposed learning algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives. Crucial problems such as adaptability, computational costs, and robustness are discussed. Experimental results illustrate that the proposed NN-based speed controller possesses a remarkable learning capability to control electric drives, virtually without requiring a priori knowledge of the plant dynamics and laborious startup procedures