A simple and fast analysis method to sort large data sets into groups with shared distinguishingcharacteristics is described and applied to single molecular break junction conductance versus electrodedisplacement data. The method, based on principal component analysis, successfully sorts data sets basedon the projection of the data onto the first or second principal component of the correlation matrix withoutthe need to assert any specific hypothesis about the expected features within the data. This is animprovement on the current correlation matrix analysis approach because it sorts data automatically,making it more objective and less time consuming, and our method is applicable to a wide range ofmultivariate data sets. Here the method is demonstrated on two systems. First, it is demonstrated onmixtures of two molecules with identical anchor groups and similar lengths, but either a π (highconductance) or a σ (low conductance) bridge. The mixed data are automatically sorted into two groupscontaining one molecule or the other. Second, it is demonstrated on break junction data measured with the πbridged molecule alone. Again, the method distinguishes between two groups. These groups are tentativelyassigned to different geometries of the molecule in the junction.