The geometry and internal parameters of artificial neural networks (ANNs) have significant effects on the prediction performance efficiency of the network. The optimal ANN geometry is problem-dependent. Although some guidance is available in the literature for the choice of geometry and internal parameters, most networks are calibrated using the trial-and-error approach. This paper presents the use of genetic algorithms (GAs) to search the optimal geometry and values of internal parameter of a multilayer feedforward back-propagation neural network (BPNN) and a radial basis function network (RBFN). The prediction performance efficiency of the GA-ANN combination is examined using an already published experimental dataset of crossflow membrane filtration. The data includes the permeate flux decline under various operating conditions (e.g. transmembrane pressure and filtration time) with different physicochemical properties of feed water (e.g. different combinations of three particle diameters, three pH values and four ionic strengths). It is illustrated that the GA-optimized ANN predicts the permeate flux decline more accurately than a network in which the ANN calibration is done using a trial-and-error approach. It is shown that scaling the training data to the range of 0-1 helps the modeler find the solution range of an RBFN for GA. (c) 2006 Elsevier B.V. All rights reserved.