A system identification problem can be formulated as an optimization task where the objective is to find a model and a set of parameters that minimize the prediction error between the measured data and the model output. The most existing system identification approaches are highly analytical and based on mathematical derivation of the system’s model. System identification is one of the most interesting applications for adaptive algorithms. We have proposed a recurrent neural network (RNN) based adaptive algorithm, due to its robustness and calculus simplicity. Based on the error signal, the filter’s coefficients are updated and corrected, in order to adapt, so the output signal has the same values as the reference signal. The proposed method is suitable for non-linear system identification.