Air particulate matter dimensions are a key air quality parameter which can be related to composition, transport properties, exposure and effects on humans and the environment. Optical particle counters are increasingly used for dynamic ambient air particle matter size characterization. Monitoring campaigns lasting several months or years produces millions of single values to be elaborated, requiring effective data treatment procedures for extracting information and knowledge. Data mining algorithms as Self-Organizing Map (SOM) can support exploratory data analysis and pattern recognition in aerosol science. The use of SOM allows to elaborate a high number of data, with powerful visualization features using 2D maps, avoiding to lose information on data variability with data pre-treatments, such as compacting minute data to hourly or daily means. In the present study we describe the optical particle counter data elaboration for particulate matter profile assessment and comparison of a long monitoring time (nearly three years) carried out near residential buildings positioned very close to a steel plant. About twelve millions recorded single values have been handled on the whole. The approach allowed to identify four main particulate matter profiles and follow their variation during the years relating the differences with changes in the plant management and process. The possible applications of the present approach are broad in the field of air quality high frequency long monitoring campaigns with different types of instruments for size and compositional characterization of both particulate matter and gases.