A malfunction of a wastewater treatment plant is a big social and biological problem. A poorly treated waste water outside the plant could provoke dangerous consequences for human beings as well as the nature itself. The conventional control systems, that are usually applied in this field, have to cope with some difficulties: complexity of the system, an ill-structured domain, qualitative information, uncertainty or approximate knowledge, real-time dynamic system... This paper shows an application of artificial intelligence in order to help the operators of wastewater treatment plants in their task of process control. The main goal is to build a knowledge-based tool useful for the diagnosis and management of wastewater treatment plants. First, is made a survey on wastewater treatment plants to describe the complexity of the system being modelled and to outline its own difficulties. It is discussed the development of the application and the methodology employed in it. A new methodology called LINNEO+ is introduced. It is used for the automatic knowledge acquisition process in order to build up a Knowledge Base. The prototype's architecture constructed -- DEPUR -- is detailed together with some obtained results.