Forecasts of future events are required in many of the activities associated with the planning and operation of the components of a water resource system. For the hydrologic component, there is a need for both short and long-term forecasts of hydrologic time series in order to optimize the system or to plan for future expansion or reduction. This paper presents the comparison of different artificial neural network (ANN) techniques in short-term continuous and intermittent daily streamflow forecasting and daily suspended sediment forecasting. Three different ANN techniques, namely, feed forward back propagation (FFBP), generalized regression neural networks (GRNN) and radial basis function-based neural networks (RBF) are applied to the hydrologic data. In general, the forecasting performance of ANN techniques is found to be superior to the other conventional statistical and stochastic methods in terms of the selected performance criteria.