Neural Networks are attractive alternative to the classical techniques for identification and control of complex physical systems, because of their ability to learn and approximate functions. This paper presents the development and implementation of adaptive Multilayer Neural Network (MNN) controller in real-time for a drive system. A MNN is first trained off-line to learn (identify) the inverse dynamics of the system, after the training is successfully completed, the MNN is used as a feedforward controller in the control scheme. The advantage of the proposed controller is that the MNN is permanently training. On-line learning is applied while the system is under control to capture any system parameter variations or disturbances. Simulation results are presented to show the advantages of adaptive MNN controller compared to non-adaptive MNN controller. Also, experimental results show that the adaptive MNN controller is able to control the speed trajectory of the chive system with a high degree of accuracy, even in the presence of disturbances.