A systematic analysis of user-dependent performance variability in the context of automatic speaker verification was first studied by Doddington et al(1998). Different categories of users were identified and labeled as sheep, goats, lambs and wolves. While this categorization is significant, it does not provide a criterion to rank the users in a database based on their variability in performance. In this work we design and evaluate a user-dependent performance criterion that requires only a limited number of client (i.e., genuine)training scores. We then extend such a study to formulate a user-specific score normalization scheme (a variant of the classical F-norm) and show that user-dependent variabilities can be reduced by employing such a scheme. The results of 13 experiments confirm the efficacy of the proposed scheme.