The application of mathematical tools can be necessary to provide an integrated analysis and interpretation of the abundant information that can be collected in air quality monitoring networks. This article develops a methodology based on the use of Self-Organizing Map (SOM) artificial neural networks for integrating data about multiple measured pollutants to group monitoring stations according to their similar air quality. The proposed method considers the subsequent geographical mapping of the clusters of stations observed with the SOM, which can make it possible to detect geographically different areas but that share similar air pollution problems. This methodology is illustrated with its application to a case study in which 517 stations of the Spanish air quality monitoring network were classified considering simultaneously their levels of regulated pollutants in 2005, highlighting some implications of data normalization in the process. In particular, the use of legal limit values to normalize the concentrations of pollutants proved to be especially advisable. Results obtained with the SOM-based methodology, when compared to classifications based directly on legislation, provided more useful classifications for further air quality management actions, and revealed that these types of tools can facilitate the design of air pollution reduction programs by discovering different areas with similar problems. © 2010 American Institute of Chemical Engineers Environ Prog, 2011.