A submerged membrane bioreactor receiving cheese whey was modeled by artificial neural network and its performance over a period of 100 days at different solids retention times was evaluated with this robust tool. A cascade-forward network was used to model the membrane bioreactor and normalization was used as a preprocessing method. The network was fed with two subsets of operational data, with two-thirds being used for training and one-third for testing the performance of the artificial neural network. The training procedure for effluent chemical oxygen demand (COD), ammonia, nitrate and total phosphate concentrations was very successful and a perfect match was obtained between the measured and the calculated concentrations. The results of the confirmation (or testing) procedure for effluent ammonia and nitrate concentrations were very successful; however, the results of the confirmation procedure for effluent COD and total phosphate concentrations were only satisfactory. (c) 2005 Elsevier B.V. All rights reserved.