Many different ATR algorithms have had their performance quantified for more than twenty years. These algorithms have been tested on data sets with significantly varying difficulty, however, the quantification of the data set difficulty has previously only been coarsely partitioned based on target operational state and the meteorological environment. Also, this has typically been done without mention of the correlation between the training and test set. In this paper we show the quantification of the signal to clutter measure (SCM) versus ATR performance, specifically the typical probabilities of detection (Pd), recognition (Pr) and false alarm rates directly. This SCM provides a basis for knowing what these performance statistics actually mean, since a 'good' or 'bad' set of performance numbers taken without quantified knowledge of the difficulty level of the data generally does not reflect the limitations or capabilities of the ATR algorithm(s) or provide an especially relevant basis for comparison.