An ANN (Artificial Neural Network) based optimizer has been built and applied to the separation of a gas flow coming out of a hydrocracking reactor. The ANN based optimizer is shown to be able to achieve a precision similar to the one of an optimizer based on a first principle model with a very significant decrease in computational time. A central point of the development of the ANN models lies in the choices related to the training procedure; suggestions about the criteria which should lead such choices have been given.