This paper deals with intelligent process control design using artificial neural networks (ANN). Neural controllers are built up and trained as inverse neural process models, using Neural Network Toolbox (NNT) in MATLAB environment. The NNT is an effective tool to handle neural network modeling problems in MATLAB interactive environment. The toolbox consists of a set of functions and structures as well as graphical user interface. Inverse plant models based neural controllers are introduced. The control performance and robustness are augmented by introducing, first, an adaptive simple integrator and, then, an intelligent controller with fuzzy integrator part. The proposed ANN control system performance is demonstrated using examples of both: control of a perturbed linear system of second order and a non-linear continuous biochemical process with simulated uncertainties.