Summary Environmental flow estimation in regulated rivers has become a major issue for watershed management in Mediterranean countries. There are many methodologies for environmental flow computation, but they usually require accurate hydrological long-term flow records, which sometimes are unavailable, and/or extensive field measurement campaigns, which can be very costly especially when the environmental flows must be determined at many locations in large basins. We analyzed the potential of neural network models for the estimation of environmental flow values in gauging sections and reaches under a natural flow regime in the watershed of the Ebro River, Spain, with a view to a future application in both ungauged and/or regime-altered sections. Non-linear multilayer feed-forward cascade-correlation neural networks were developed to model the relationships between known environmental flows (Qb calculated) and two sets of independent variables related to physical and hydrological watershed characteristics or to the general flow regime. Three models were found capable of good estimations of environmental flows, based on variables such as the 10-year average of the lower monthly flows, the mean value of the length of the period in days with flows continually below the 40% of the mean annual flow (spell duration), and the flow equaled or exceeded 270 days per year (Q270). Correlation coefficients ( r) between calculated and estimated values were high (>0.90), and average absolute errors were low (<0.44 m 3/s) for the three models. The limited number of variables in the models (just two) was considered very promising for operational application of the model to ungauged or regime-altered sections. Results suggest that artificial neural network models can be simple, robust, reliable and cost-efficient tools for environmental flow determination at the watershed level.