The effect of employment of different methods of suspended sediment estimation by artificial neural networks (ANNs) was the concern of the presented study. It was seen that the initial statistical analysis of flow and sediment data provided valuable information about the appropriate number of input nodes of the neural network, thereby avoiding redundant nodes. The k-fold partitioning of the training data set showed that similar or even superior sediment estimation performances can be obtained with quite limited data provided that the training data statistics of the subset are close to those of the testing data. The range-dependent neural network (RDNN) was found to be superior to conventional ANN applications, where only a single network is trained considering the entire training data set. It was seen that both low and high-observed sediment values were closely approximated by the RDNN. (c) 2005 Elsevier B.V. All rights reserved.