Correspondence analysis (CA), a multivariate descriptive data analytic method, is a statistical tool for the graphical analysis of contingency tables. Multiple correspondence analyses (MCA) is an extension of the CA technique that allows the analysis to cover more than 2 categorical variables. This exploratory method simplifies and reveals patterns in complex data sets, allowing identification of relationships between variables of interest. The detailed and descriptive analysis obtained with MCA is a potential advantage in studies in which a large amount of qualitative data is collected. Our objective was to describe herd characteristics and management practices that allow pattern recognition in mastitis outbreaks. In the present study, MCA was applied to assess mastitis risk factors in 39 dairy herds in 3 different regions of Portugal. A 38-item questionnaire regarding herd characteristics, milking procedures, mastitis control and biosecurity practices was administered, after which a MCA was performed. The results showed that each region had a particular pattern of management practices and associations between some procedures or routines were identified (3 management practices clusters). The most influential variables were related to the pasture-based farming system studied, frequency of addition of bedding, stall base material, mastitis record-keeping and specific milking procedures such as wearing gloves, pre-dipping, and the method of cleaning and drying teats. Through MCA it was possible to single out region-specific weaknesses which allowed the development and adoption of tailored mastitis control programs. A strong correlation was observed between the presence of a mastitis control program and the utilization of routine management practices. Producers who ask for veterinary advice regarding udder health and mastitis control, acquire a deeper understanding and move toward applying the recommended procedures. This is likely the main reason why a markedly different pattern between regions was observed. MCA technique can be helpful not only mastitis control and milk quality research, but also for other aspects of dairy science.