Abstract: There are many forecasting models, but not all of them are able to monthly inflow forecasting. In this study, the abilities of static and dynamic artificial neural network model for Dez reservoir inflow forecasting compared. The 47-years monthly discharges used, so that first 42 years and last 5 years used for model training and models forecasting phase, respectively. Different structures of the static and dynamic models of artificial neural network model investigated in terms of RMSE index. Firstly, by using data from October 1960 to September 2002 the best structures of static and dynamic neural networks are determined. Therefore, the Dez reservoir monthly inflow are forecasted and compared with observed data October 2002 to September 2007 based on optimized data. Furthermore, two transfer functions, radial and sigmoid and different neurons in hidden layer were evaluated. The results show that the best Dez reservoir inflow forecasting model is autoregressive artificial neural network model using sigmoid activity function with 17 neurons in hidden layer. Autoregressive artificial neural network model with sigmoid activity function were able to forecast next 5 years Dez inflow.