This paper proposes the use of a Case-Based Reasoning (CBR) system for the control and the supervision of a real wastewater treatment plant (WWTP). A WWTP is a critical system which aims to ensure the quality of the water discharged to the receiving bodies, stablished by applicable regulations. At the current stage the proposed methodology has been tested off-line on a real system for the control of the aeration process in the biological treatment of a WWTP within the ambit ofConsorci Besòs Tordera (CBT), a local water administration in the area of Barcelona. For this purpose, data mining methods are considered to extract the available knowledge from historical data to find a useful case base to be able to generate set-points for the local controllers in the WWTP. The results presented in this work are evaluated taking into account the performance of the CBR method e.g. case base size, CBR cycle time or number of cases resolved satisfactorily (forthcoming steps will include on-line tests). For this purpose, some Key Performance Indicators (KPI) are designed together with the plant manager and process experts, in order to monitor key parameters of the WWTP which are representative of the performance of the control and supervision system. Hence, these KPI are related with water quality regulations —e.g. ammonia concentration in the WWTP effluent— and the economic cost efficiency —e.g. electrical consumption of the installation. In order to evaluate the results, different flat-based memory organizations (i.e. cases are stored sequentially in a list) for the case base are considered. First, a unique case base is used. At the current stage and for the results shown in this work, this case base is divided in multiple libraries depending on a case classification. Finally, the combination of this approach with Rule-Based Reasoning (RBR) methods is proposed for the next stages of the work.