Abstract: A self-tuning fractional order PID controller based on hybrid PSO and neural networks is presented. It consists of three parts. In the first part, a fractional order PID controller directly controls the object. In the second part, a group of fractional order PID parameters are obtained by hybrid PSO. The third part, RBF neural networks is used to optimize and adjust the fractional order PID parameters on-line by exploiting the nonlinear mapping capabilities of neural networks and training fast and get Jacobian information for the object. The simulation results show that the controller has a fast response speed, high convergence and robustness. It can be used to control different objects and processes, specially complex, non-determination mathematical model control system.