This study explores the potential of integrating two different artificial intelligence techniques, namely neural network and fuzzy logic, effectively to model the rainfall-runoff process from rainfall and runoff information. The integration is achieved through representing fuzzy system computations in a generic artificial neural network (ANN) architecture, which is functionally equivalent to a fuzzy inference system. The model is initialized by a hyperellipsoidal fuzzy clustering (HEC) procedure, which identifies suitable numbers of fuzzy if-then rules through proper partition of the input space. The parameters of the membership functions are optimized using a nonlinear optimization procedure. The consequent functions are chosen to be linear in their parameters, and a standard least squares error method is employed for parameter estimation. The proposed model is tested on two case studies: Narmada basin in India and Kentucky basin in the United States. The results are highly encouraging as the model is able to explain more than 92% of the variance. The performance of the proposed model is found to be comparable to that of an adaptive neural based fuzzy inference system (ANFIS) developed for both the basins. The number of parameters in the proposed model is fewer compared to ANFIS, and the former can be trained in lesser time. It is also observed that the proposed model simulates the peak flow better than ANFIS. Overall, the study suggests that the proposed model can potentially be a viable alternative to ANFIS for use as an operational tool for rainfall runoff modeling purposes. Copyright 2007 by the American Geophysical Union.