Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their s implicity and robustness. The main application problems are t he tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal co ntrol performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques a s Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and ov ershoot. In this study, suitable controllers able o f improving timing performance of second order plants are propo sed. The results show that the PID controller has g ood overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of sett ling time and a very good overshoot value. The neural-fu zzy controller gives the best timing parameters improving the control performances of the others tw o approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.