This paper deals with neural-predictive algorithm for some nonlinear
processes in the industry. Neural model predictive control (NMPC) uses artificial neural networks (ANN) for modeling the process and for configuration of the optimizer. The optimizer sets up on-line controller parameters by predicting next control action signals. Depending on the number of prediction steps, the optimizer can predict the process behavior in the future. Therefore this type of predictive control is very useful for the control of the highly nonlinear processes, which are known for their various behaviors. One practical example is the isothermal polymerization reactor where the NMPC Controls the oiitput variable very robustly. Finally, this control method is compared with the linear PID controller designed to solve this problém using a genetic algorithm.