Distance between a data point and a mulitivariate space's centroid (overall mean). Use the Mahalanobis distance in Principle Components Analysis to identify outliers. It is a more powerful multivariate method for detecting outliers than examining one variable at a time because it takes into account the different scales between variables and the correlations among them.
The circled data point on the scatterplot clearly does not fit in with the two variables' correlation structure. However, when examined individually, neither its x or y-value are unusual. Nevertheless, the Mahalanobis distance for this point is unusually large.