The evaporation is one of the basic components of the hydrologic cycle and it is essential for studies such as water balance, irrigation system design and water resource management, and it requires knowledge of many climatic variables. Although, there are many empirical formulas available for evaporation estimate, but their performances are not all satisfactory due to the complicated nature of the evaporation process. Accordingly, this paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from HAMA by using temperature, relative humidity and wind velocity. The mathematical model was built by the (nntool-box), which is one of the MATLAB tools. The feed forward back propagation network with one hidden layer has been utilised to construct the model. Different networks with different number of neurons were evaluated. Root Mean Squared Error (RMSE) was employed to evaluate the accuracy of the proposed model. The study shows that ANN (3-14-1) was the best model with RMSE (21.5mm/month) and R2 (0.97). This study suggests using other types of neural networks for estimation of evaporation