Validation of automatic target recognition (ATR) algorithm needs huge amount of real data, which is mostly infeasible. Hence we need statistical separability indices (SI) to evaluate the performance of ATR algorithms using limited amount of data. In this paper we explain five such different SIs. For parametric classifiers, we use the classic Bhattacharya distance as the SI and propose a simpler modified Bhattacharya distance. For non-parametric schemes we use the classic geometrical SI and propose two new geometrical SIs, viz. modified geometrical SI and nearest neighbor based separability index. The utilities and implications of these SIs are demonstrated by using them in real ATR exercises.