As utilities move towards process automation and unattended operations, the drinkingwater treatment industry must actively search out new technologies that allow for theoptimization of process control strategies. When used to assist in the control of water treatmentprocesses, artificial neural networks (ANNs) have the potential to significantly reduce operationsand maintenance costs, to improve customer service and water quality. This paperpresents the results of ANN models developed to predict the removal of particulate matter at theF.E. Weymouth Filtration Plant (Weymouth) and the Oxidation Demonstration Project (ODP)Plant, owned and operated by the Metropolitan Water District of Southern California (MWD) of La Verne, California.The models derived from this research, which is being funded by the American Water WorksAssociation Research Foundation (AWWARF), can be incorporated into indirect process controlschemes at the MWD facilities. The indirect process control applications, including virtuallaboratories and operator training modules, can assist operators at MWD facilities in optimizingparticle removal. Includes 13 references, tables, figures.