This paper proposes a novel procedure based on a benchmarking model of data envelopment analysis (DEA) to solve the two-group classification problem. The idea was inspired by the fact that benchmarks dominate their own production possibility set (PPS). If observations belong to the same group, they should be in the same PPS and dominated by the same benchmarks. Therefore, the benchmarks of two groups were identified to construct a pair of nonlinear discriminant frontiers without pre-specifying the classification function form as in other parametric discriminant analysis (DA) approaches. Since the discriminant frontiers are established by the boundary of two groups, the proposed procedure differs from some existing discriminant approaches that establish the discriminant function by minimizing the total deviation of all observations from their group mean. Two applications are illustrated to support the validation of the proposed procedure even though the data sets may have outliers or an unequal number of observations.