A simplified hybrid neural net approach was applied for the modeling and subsequent analysis of a chemical waste water treatment plant in Mizushima, Japan. The objective of this study is to reduce the occurrences of overflow in the clarifier caused by filamentous bulking and thereby increase waste water treatment capacity. Sensitivity analysis showed that temperature, activated sludge age and ammonia concentration were the most sensitive variables to the clarifier interface level. Sludge age control and a nutrient control strategy that utilises an ammonia software sensor are proposed. In addition, on-line diagnostics and optimisation are discussed.