A neural network approach for the prediction of the rate of ultrafiltration of proteins has been developed. The approach has been used to predict the rate of ultrafiltration of bovine serum albumin as a function of pH and ionic strength. This is a very non-linear problem that has previously been best described through sophisticated descriptions of protein-protein interactions within the layer close to the membrane surface. Networks with a single hidden layer have been used to predict the dynamic rate of filtration from very few data points. Emphasis has been placed on using a small number of training data points and small networks. Variation of the number of training points and use of different training point selection schemes have shown that it is the quality of training points rather than the quantity that leads to the best predictions. The network training process may be optimised by using physical insights to select appropriate input variables. Testing of the neural network approach showed that it could give excellent agreement with experimental results, with average errors less than 2.7%.