In this study applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, have been investigated using monthly streamflow records of two successive stations on Çoruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back propagation neural network algorithm (FFBP) has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression (GRNN) and radial basis function (RBF) algorithms were used to generate two new ANN models for the selected scenario. Ultimately a comparative performance study between the different algorithms has been performed using Nash-Sutcliffe efficiency, squared correlation coefficient, and root mean square error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only one month-lagged record of both stations was satisfactory to achieve accurate models with high efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.