Herein, a back-propagation artificial neural network (BP-ANN), a fit and predictive tool and suitable for bridging the inputs and outputs of a non-linear problem, is used to model the adsorption of bovine serum albumin (BSA) on porous polyethylene (PE) membrane. Based on the adsorption data from FTIR-mapping, the parameters of the neural network with a hidden layer are determined by a trial-and-error method. There is a good agreement between the predicted results by BP-ANN and the experimental data, and the interpolative predictions by BP-ANN are more precise than those by convectional diffusion equation. Though BP-ANN cannot provide detail information concerning the mechanism like a conventional diffusion equation (which can permit an evaluation of diffusion coefficient or mass transfer coefficient), it is a powerful predictive tool as well as a useful compensation to conventional diffusion equation when dealing with the problems of which the mechanism is not entirely understood or very complex. (c) 2004 Elsevier B.V. All rights reserved.