A nonlinear approach for recovering an undistorted signal which is distorted by any system is presented. This method is based on the techniques of neural networks. When trained with the original signal as target and the distorted signal as input vector, a neural network can be used to estimate the original signal instantly and can be used in real time for online signal recovery. The proposed approach has been tested in the case when the original signal is a random binary time sequence and the distortion is obtained by passing the original signal through a low-pass filter plus a white noise. It is shown that this approach provides excellent recovery of the original signal.<<ETX>>