Daily, weekly, ten-daily, and monthly monsoon runoff and sediment yield from an Indian catchment were simulated using back propagation artificial neural network (BPANN) technique, and the results compared with the observed and with those due to single- and multi-input linear transfer function models. Normalising the input by its maximum for both the pattern and batch learning algorithms in BPANN, the model parsimony was achieved through network pruning utilising error sensitivity to weight a criterion, and it was generalised through cross-validation. The performance based on correlation coefficient and coefficient of efficiency suggested the pattern-learned artificial neural network (ANN) based runoff simulation to be superior to both single- and multi-input models in calibration. The single-input models were however superior in verification. The ANN based sediment-yield models performed better than both single- and multi-input models in calibration as well as cross-validation/verification.