The possible role of artificial neural networks to model the impact of the distribution network on water quality under different patterns of demand and for different source water qualities is considered and contrasted against a deterministic approach. To investigate the feasibility of this technique, a case study is presented of the prediction of oxidation reduction potential at a single point and at multiple points within a distribution network using hydraulic and water quality data collected at the inlet to the network. Based on the results of the study, the advantages and disadvantages of an artificial neural network approach and a deterministic approach to the prediction of water quality in distribution are identified. The study has demonstrated the viability of the application of artificial neural networks to predict water quality changes in distribution, and it has been shown that water quality changes within the distribution network are essentially flow-driven. The potential of this technique to evaluate the relative importance of parameters prior to the formulation of a deterministic model was highlighted. The need to complete further work to ascertain the extent of data required to achieve a given accuracy of prediction by an artificial neural network in this particular role has also been identified.