The applicability of semi-empirical and artificial neural network (ANN) modeling techniques for predicting the characteristics of a microfiltration system was assessed. Flux decline under various operating parameters in cross-flow microfiltration of BSA (bovine serum albumin) was measured. Two hydrophobic membranes were used: PES (polyethersulfone) and MCE (mixed cellulose ester) with average pore diameters of 0.22 mu m and 0.45 mu m, respectively. The experiments were carried out to investigate the effect of protein solution concentration and pH, trans-membrane pressure (IMP). cross-flow velocity (CFV), and membrane pore size on the trend of flux decline and membrane rejection at constant trans-membrane pressure and ambient temperature. Subsequently, the experimental flux data were modeled using both classical pore blocking and feed forward ANN models. Semi-empirical models based on classic mechanisms of fouling have been proposed. It was shown that these mechanisms could predict the microfiltration flux for a specified period of processing time; while through appropriate selection of ANN parameters such as the network structure and training algorithm, the ANN-based models are competent in modeling membrane filtration systems for all operating conditions and the entire filtration time with desired accuracy. (C) 2011 Elsevier B.V. All rights reserved.