The paper proposes a two-phasemethodology to support groups inmulticriteria classification problems. The first phase, which relies on a dominance-based rough set approach (DRSA), takes a set of assignment examples as input and outputs a set of collective decision rules, representing a generalized description of the decisionmakers' preference information. The second phase then applies these collective decision rules to classify all decision objects. The methodology uses “if… then …” aggregation rules that coherently implement the majority principle and veto effect. The aggregation rules thus allow obtaining consensual decisions. Furthermore, the contribution of each decisionmaker to the collective decision is objectivelymeasured by the quality of individual classification conducted by this decision maker during the first phase. The methodology has been validated by developing a prototype and applied to a nuclear risk management decision problem