Climate change has influenced several of the water cycle related variables such as rainfall that contribute to increasing natural disasters. To establish new methodologies for rivers level forecasting is necessary for the implementation of early warning systems. In this work, we present results of a multilayer perceptron artificial neural network (ANN) to forecast temporal series of water levels at the outlet of Rio Negro river with 24-hour antecedence. Input data was collected by a set of hydrological monitoring stations composed of water level and rainfall measures acquired with a one-day resolution. Water-level prediction were evaluated by the Nash-Sutcliffe coefficient (NSE) and by the root mean square error (RMSE). The results show consistency between predicted and observed values, especially when combining both water level and rainfall data. In such case, values of NSE reached 0.93 to 0.54 and RMSE between 0.028 and 0.061 for antecedence of 1 to 7 days respectively with implemented topology for the empirical model.