Identification and control of nonlinear processes can be achieved using neural networks. However, previous work extending the identification to the on-line adaptive case has resulted in extremely poor performance, preventing practical application in a control framework. The work presented in this paper proposes and demonstrates a powerful method for implementing a neural network model of nonlinear process dynamics for adaptive control. The performance of adaptation has now been sufficiently raised to allow practical adaptive control to be considered. The new adaptive method has been amalgamated with multistep nonlinear predictive control techniques to form an adaptive neural controller. The performance of this controller is demonstrated, and evaluated using two simulated realistic processes; level control of a conical tank and multivariable control of an industrial evaporator. The results indicate that these techniques have good practical potential for the adaptive control of nonlinear processes.