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Wastewater treatment systems have, over the past decades, been subjects for optimization and control research. One of the most intricate problems faced is that direct measurements of the variables of interest are seldom available. A large part of research has therefore been aimed at the extraction of suitable information from indirect measurements such as dissolved oxygen, pH, and oxidation reduction potential (ORP). Even if relatively complex tools, such as neural networks and fuzzy logic, have been used to conceive control laws, advantage is seldom taken of such tools with respect to development of the actual control algorithm. In this paper, a simple yet effective tool is presented that allows the detection of a desired process state by means of the Hotelling’s T2statistic. The detection tool is generic in nature and is thereby applicable to any process where a certain desired state is to be detected by means of measured variables reflecting the targeted state. Its advantages over formerly proposed control strategies are discussed, and the precautions that were taken to render its application robust are presented. It is shown by means of a laboratory-scale sequencing batch reactor (SBR) setup for nutrient removal from wastewater that the proposed controller allows one to detect the targeted endogenous state and that its application leads to effective optimization of the overall system performance. More specifically, the length of the optimized phase is reduced by 41% of its original default length and a reduction of 5% is estimated for the expected energy consumption by the aeration system. In addition, effluent concentrations of total nitrogen and nitrate nitrogen are estimated to be lower by 30 and 25%, respectively. This is attributed to the gained length of the anoxic phase subsequent to the aerobic phase. [ABSTRACT FROM AUTHOR]