Progress in molecular structure determination by cryo electron microscopy and single particle analysis has led to improvements in the resolution achievable. However, in many cases the limiting factor is structural heterogeneity of the sample. To address this problem, we have developed a method based on statistical analysis of the two-dimensional images to detect and sort localised structural variations caused, for example, by variable occupancy of a ligand. Images are sorted by two consecutive stages of multivariate statistical analysis (MSA) to dissect out the two main sources of variation, namely out of plane orientation and local structural changes. Heterogeneity caused by local changes is detected by MSA that reveals significant peaks in the higher order eigenimages. The eigenimages revealing local peaks are used for automated classification. Evaluation of differences between classes allows discrimination of molecular images with and without ligand. This method is very rapid, independent of any initial three-dimensional model, and can detect even minor subpopulations in an image ensemble. A strategy for using this technique was developed on model data sets. Here, we demonstrate the successful application of this method to both model and real EM data on chaperonin-substrate and ribosome-ligand complexes.