Artificial neural networks (ANNs) have been proven to be very successful in dealing with complicated non-linear problems. In this paper, ANNs are adopted to forecast daily flow in the lower reach of the Weihe River. The primary objective of this study is to investigate the possibility to integrate more temporal and spatial information in daily flow forecasting models, which is not easily attained in the traditional time-series models. In order to achieve this objective, correlation analysis is firstly made in this paper. Furthermore, several issues with ANN model application, e.g. the determination of hidden layer nodes and training iterations, are discussed. Model performance is analyzed and some results are presented. Model calibration and verification show that the precision of the ANN model is obviously higher than those of the traditional statistical models.