We offer a straightforward framework for measurement of progress, across many dimensions, using cross-national social indices, which we classify as linear combinations of multivariate country level data onto a univariate score. We suggest a Bayesian approach which yields probabilistic (confidence type) intervals for the point estimates of country scores--a vital, and often missing, feature in cross-national comparisons. We demonstrate our approach using the United Nations Development Programme's Millennium Development Goals (MDGs), via the Maternal and Neonatal Program Effort Index (MNPI) data (Ross et al. in "Trop Med Inter Health" 6(10):787-798, 2001), and Human Development Index (HDI) (2010) as examples.