A predictive control strategy for nonlinear processes based on radial basis function models is proposed. First, a radial basis function model of the process is developed using stepwise regression and least squares estimation. This model is then used to train a nonlinear predictive controller, which is also implemented as a radial basis function network. Since no optimization problems have to be solved on-line, this control strategy can be implemented easily. The proposed strategy is applied to an experimental pH neutralization process; it provides both excellent setpoint tracking and disturbance rejection when compared to conventional PI control.