This work proposes the development of an intelligent predictive controller. Recurrent neural networks are used to identify the process, providing predictions about its behavior, based on control actions applied to the system. These information are then used by fuzzy controllers to accomplish a better control performance. Moreover, the fuzzy controller membership functions are evolved by Genetic algorithms (GNs) allowing an automatic tune of controllers. The combined use of these techniques make possible the control of multi-variable processes using several fuzzy controllers where the coupling among controlled variables are modeled by neural networks, and control objectives can be inserted into the GA fitness function. The methodology was applied to a simulation of the startup of a continuous distillation column. This process is chosen due to their characteristics, such as inertia, large accommodation time and conflicting control objectives that make these processes hard to control with traditional methods. (c) 2005 Elsevier Ltd. All rights reserved.