Artificial neural networks (ANNs) are tested for the output updating of one-day-ahead and three-day-ahead streamflow forecasts derived from three lumped conceptual rainfall-runoff (R-R) models: the GR4J, the IHAC and the TOPMO. ANN output updating proved superior to Yang and Michel's (2000) parameter updating scheme and to the 'simple' output updating scheme, which always replicates the last observed forecast error. In fact, ANN output updating was able to compensate for large differences in the initial performance of the three tested lumped conceptual R-R models, which the other tested updating approaches could not achieve. This is mainly implemented by considering input vectors usually exploited for ANN R-R modeling such as previous rainfall and streamflow observations, in addition to the previous observed error. For one-day-ahead forecasts, the performance of all three lumped conceptual R-R models, used in conjunction with ANN output updating, was equivalent to that of the ANN R-R model. For three-day-ahead forecasts, the performance of the ANN-output-updated conceptual models was even superior to that of the ANN R-R model, revealing that the conceptual models are probably performing some tasks that the ANN R-R model cannot map. However, further testing is needed to substantiate this last statement.