The Minimum Spanning Tree (MST) histogram is a multivariate extension of the ideas behind the conventional scalar rank histogram. It tabulates the frequencies over n forecast occasions of the rank of the MST length for each ensemble, within the group of such lengths that is obtained by substituting an observation for each of its ensemble members in turn. In raw form it is unable to distinguish ensemble bias from ensemble underdispersion or to discern the contributions of forecast variables with small variance. The use of scaled and de-biased MST histograms to diagnose attributes of ensemble forecasts is illustrated both for synthetic Gaussian ensembles and for a small sample of actual ensemble forecasts.