Prediction of the dynamic crossflow ultrafiltration rate of colloids poses a complex non-linear problem as the filtration rate has a strong dependence on both the solution physicochemical conditions and the operating conditions. As a result, the development of general physics-based models has proved extremely challenging. In this paper an alternative artificial neural network approach is developed. The approach has been used to predict the time-dependent rate of ultrafiltration of silica suspensions under different conditions of pH, ionic strength and applied pressure. Neural networks with a single hidden layer were used to predict the filtrate flux-filtration time profiles from a small number of training points. Training points were chosen from both three and five sets of solution conditions to study how network predictability would be affected. Physical understanding of the process helped in choosing the right input variables, which in turn optimised the training. The neural network approach was found to be capable of modelling this complex process accurately.