The forecasting of the meteorologic data carries significance for meteorology and water resources engineering. The forecasting of rainfall particularly is complicated because the data is intermittent, i.e. the observed time series data contains zero values as well as the positive non-zero observations. The available meteorologic models having physical basis are difficult to establish since they require tremendous amount of data for calibration. In this study artificial neural networks are employed to forecast the rainfall data using with and without periodic components. The forecasting results are evaluated in terms of mean square error (MSE) and the total rainfall for the testing period. It is shown that the forecasted series capture the general behaviour of the observed one and compare well in terms of the rainfall total.