The concept of employing Common Effluent Treatment Plants (CETPs) to collectively treat effluents emanating from a number of industries is assuming rapid significance due to industrialization and expected reduced cost of collective treatment. Modeling of process variables in CETPs is helpful in controlling its dynamic behavior and for predicting plant performances. Determination of real-time data is costly requiring lab and skilled personnel, besides having limitations on measurement of real-time process variables such as BOD. Under such circumstances, process modeling could be gainfully employed. CETPs have time-varying dynamics that is non-linear, making conventional modeling techniques ineffective, and necessitating use of techniques such as Artificial Neural Networks (ANN). This Book employs ANN to evolve a framework wherein advance predictions of CETP performances can be made, and applies ANN models to the case of a CETP at Bhopal, India. It is expected that this Book would be of use to Researchers, CETP Plant Operators/Managers, Regulatory authorities such as the Pollution Control Boards/Agencies, and to individual industries who intend to join the CETPs.