Large volumes of runoff during storm periods makes serious flood hazard a frequently occurring problem in Taiwan, therefore, studies on rainfall-runoff simulation are important tasks for flood mitigation programmes. In recent years, hydrologists have demonstrated the great potential of using artificial neural networks for flood forecasting from rainfall-runoff studies. In this study, a back-propagation artificial neural network was used to simulate three storm events on an upstream section of Ta-chia river basin. Scenarios were designed with differnt combinations of rainfall and runoff using data from several gauges. The preliminary result shows that the model can forecast the magnitude and time of peak flow accurately when the leadtime is less than 2 hours. At the same time, the research implies that accurate modeling needs not only rainfall and runoff measurement values but also the other hydrological inputs using a network of gauges produces a more accurate prediction than rainfall inputs calculated with these original data. In particular, in larger watersheds, rainfall inputs using a network of gauges produces a more accurate prediction than area weighted techniques using Theissen polygons. Inaccuracies in estimating the rate of flow using the rising and falling limbs of the predicted flood response hydrograph occurred because of the limited availability of storm events data in this study. Further research is recommended using a genetic algorithm to optimize the selection of input variables to the neural model. Soil moisture and landuse conditions in the watershed are also suggested for consideration in the neural model in further research.