The main environmental problems comprise two main directions: air and water. Water treatment plant is a critical infrastructure, especially in large cities. The activated sludge process is a typical example of highly nonlinear system. The associated models found in literature are mainly analytical and complex. In this paper there are proposed data-driven models obtained from plant operation data. A factor reduction procedure, namely principal component analysis is used to find meaningful correlations between process measurements. The selected correlations are obtained via simple and multiple regression algorithm. The resulted models are specific to the studied plant, simpler than the analytical ones, and with sufficient accuracy if used in plant monitoring and operation. The proposed procedure of using data-driven models for inferential measuring decreases the analysis costs (even eliminating the necessity of measuring equipment). If the experience in operating the plant is used to predict parameter trends this procedure can provide a useful tool for developing a decision support system for the plant operator. A real time prediction module associated with a warning system can be applied for every active sludge process, having as condition the availability of plant operating data.