The uncertainty caused by the discontinuous nature of wind energy affects the power grid. Hence, forecasting the behavior of this renewable resource is important for energy managers and electricity traders to overcome the risk of unpredictability and to provide reliability for the grid. The objective of this paper is to employ and compare the potential of various artificial neural network structures of multi-layer perceptron (MLP) and radial basis function for prediction of the wind velocity time series in Tehran, Iran. Structure analysis and performance evaluations of the established networks indicate that the MLP network with a 4-7-13-1 architecture is superior to others. The best networks were deployed to unseen data and were capable of predicting the velocity time series via using the sliding window technique successfully. Applying the statistical indices with the predicted and the actual test data resulted in acceptable RMSE, MSE and R² values with 1.19, 1.43 and 0.85, respectively, for the best network.