Abstract: River flow forecasting is required to provide important information on a wide range of cases related to design and operation of river systems. Since there are a lot of parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically-based models is often a difficult and time consuming procedure. So it is preferred to implement a heuristic black box model to perform a non-linear mapping between the input and output spaces without detailed consideration of the internal structure of the physical process. In this study, the capability of artificial neural networks for stream flow forecasting in Kashkan River in West of Iran is investigated and compared to a NAM model which is a lumped conceptual model with shuffled complex evolution algorithm for auto calibration. Multi Layer Perceptron and Radial Basis Function neural networks are introduced and implemented. The results show that the discharge can be more adequately forecasted by Multi Layer Perceptron neural network, compared to other implemented models, in case of both peak discharge and base flow forecasting.