Abstract: A new fast learning algorithm for recurrent neural networks is proposed. By introducing the signal flow graphs technique, it overcomes the disadvantage of complexity of the gradient of the error function. And for more fast convergence, the BFGS method is used. Simulation results show that the proposed algorithm converges faster than the traditional algorithm.