The general multitarget detection (or open-set identification) task is the intersection of the more common tasks of close-set identification and open-set verification/detection. In this task, a bank of parallel detectors process an input and must decide if the input is from one of the target classes and, if so, which one (or a small set containing the true one). In this paper, we analyze theoretically and empirically the behavior of a multitarget detector and relate the identification confusion error and the miss and false alarm detection errors in predicting performance. We show analytically that the performance of a multitarget detector can be predicted from single detector performance using speaker and language recognition data and experiments.