The quality of treated wastewater has always been an important issue, but it becomes even more critical as human populations increase. Unfortunately, current ability to monitor and control effluent quality from a wastewater treatment process is primitive (Wen & Vassiliadis, 1998). Control is difficult because wastewater treatment consists of complex multivariate processes with nonlinear relationships and time varying dynamics. Consequently, there is a critical need for forecasting models that are effective in predicting wastewater effluent quality. Using data from an urban wastewater treatment plant, we tested several linear and nonlinear models, including ARIMA and neural networks. Our results provide evidence that a nonlinear neural network time series model achieves the most accurate forecast of wastewater effluent quality.