We introduce a feature ranking approach specific to clustering with finite mixture models and consider its potential usage in a variable selection context. Our approach focuses on treating the log-likelihood as the optimization target, and measures changes therein upon marginal permutation of the variables. We apply this approach to several real and simulated data sets, and include discussion of usage with stability selection and other broadly applicable variable selection techniques.