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Automated recognition is increasingly used to extract information about species vocalizations from audio recordings. During processing, recognizers calculate the probability of correct classification (“score”) for each acoustic signal assessed. Our goal was to investigate the implications of recognizer score for ecological research and monitoring. We trained four recognizers with clips of Common Nighthawk (Chordeiles minor) calls recorded at different distances: near, midrange, far, and mixed distances. We found distance explained 49% and 41% of the variation in score for the near and mixed-distance recognizers, but only 3% and 6% of the variation for the midrange and far recognizers. We calculated detection functions for each of the recognizers at various score thresholds and found that the detection function for the near and mixed-distance recognizers satisfied the assumptions of density estimation for most …