The present work reports a new control methodology based on proportional, integral, and derivative (PID) control algorithms conjugated with feedforward artificial neural networks (FAANs). The FANNs were used as predicted models of the controlled variable. This information is transferred to PID controllers, through the readjustment of the pre-established setpoint. The proposed methodology was tested generally for a first order system using a PI controller, a second order system using a PI control, a second order system in series with a first order system using a cascade control structure. The problem of the reaction temperature control in a batch-jacketed reactor with a cascade control structure was also analysed as a particular case. The simulation results shows that better control performances are achieved when the control methodology presented in this work is used as a complementary tool of the PID-based control algorithms.