In works of great responsibility (ports, dams, nuclear power, etc.), the amount of instrumentation data may allow the construction of models for the temporary variability of the properties of interest based on neural network techniques. In case of dams, the monitoring through the installation of an instrumentation system plays a fundamental part in the evaluation of the behavior of these structures, during the construction period as well as in the operation period. In this work the technique of temporal neural networks (TNN) was used for analysis, modeling and forecast of the water discharges values in the Funil dam, from Furnas Centrais Elétricas system, starting from the data of available instrumentation in the period between 02/09/1985 and 25/02/2002. The temporal neural networks used in this research were the following: TNN with feedforward architecture and the windowing technique, recursive TNN Elman, TNN FIR and TNN Jordan. Two additional techniques (Box & Jenkins and geostatistical models) were employed for analysis of the time series with the purpose to compare the results obtained with neural networks. In this research the generation of confidence intervals for TNN and geostatistical methods were also investigated. The discharge values forecasts analyzed in this work for the Funil dam presented satisfactory results, with respect to the neural network, Box & Jenkins and geostatistical methods.