To build advanced controllers one needs some arbitrary simplified models of the processes to be controlled. To overcome this situation, we are investigating the use of neural network techniques. The first problem is how to include dynamics in a neural net. We have considered recurrent neural networks and dynamical neurons. We have implemented an extended backpropagation to take into account the dynamics in the training phase. Then, a general method has been developed to build neural advanced controllers. This approach is based on the minimization of a closed-loop control criterion and on an architecture in which some control knowledge can be integrated. As a test example, we have chosen to control the pH of a mixture of a strong acid and a strong base; the strong non-linearity of this process results from the static gain being highly variable when the pH is around 7. The control performance of the neural controller is then compared with a geometric non-linear control. Finally, we show, in our example, that some neural network parameters can be tuned as can parameters in classical controllers.