An overview of the recent developments of neural network modeling is presented along with its use in model predictive control (MPC). A nonlinear process example is introduced to demonstrate the application of various approaches based on neural networks and evaluate their performance in terms of closed-loop stability and prediction accuracy. Finally, the paper concludes with a proposal of future research directions on neural network modeling and its integration in theory of control.