The economic literature acknowledges that market factors and corporate decisions may increase the likelihood that various forms of collusive agreements will be successful. In order to identify collusive behaviours, the European Commission proposes merger control and anti-cartel decisions. The European Commission's actions are occasionally seen as case-driven, and antitrust intervention is frequently carried out too slowly. We propose a novel approach to evaluate the features of a manufacturing sector that can be considered as a warning for future infractions. We investigate, in terms of decision rules, the relationship between risk indicators and the detection of collusive behaviour using the Dominance-based Rough Set Approach (DRSA). Data refer to various institutional sources concerning different manufacturing sectors from 5 countries (France, Germany, Italy, Spain and United Kingdom). Making use of a "Hybrid" information system, taking into account the conditional attributes' original numerical values, and classifying decision attributes in a less granular way, allows a more straightforward interpretation of the results. The size of the firm and market concentration seem to be the most crucial factors when it comes to mergers, while unexpectedly, market stability and asymmetry seem to be less important for spotting collusion
