The capability of a radial basis function neural network (RBFNN) to predict long-term permeate flux decline in crossflow membrane filtration was investigated. Operating conditions of transmembrane pressure and filtration time along with feed water parameters such as particle radius, solution pH, and ionic strength were used as inputs to predict the permeate flux. Simulation results indicated that a single RBFNN accurately predicted the permeate flux decline under various experimental conditions of colloidal membrane filtrations and eventually produced better predictability than those of the regular multi-layer feed-forward backpropagation neural network (BPNN) and the multiple regression (MR) method. We believe further development of the artificial neural network approach will enable us to design and analyze full-scale processes from results of laboratory and/or pilot-scale experiments.