Reliance on Automated Target Recognition (ATR) technology is essential to the future success of Intelligence, Surveillance, and Reconnaissance (ISR) missions. Although benefits may be realized through ATR processing of a single data source, fusion of information across multiple images and multiple sensors promises significant performance gains. A major challenge, as ATR fusion technologies mature, is the establishment of sound methods for evaluating ATR performance in the context of data fusion. The Deputy Under Secretary of Defense for Science and Technology (DUSD/S&T), as part of their ongoing ATR Program, has sponsored an effort to develop and demonstrate methods for evaluating ATR algorithms that utilize multiple data source, i.e., fusion-based ATR. This paper presents results from this program, focusing on the target detection and cueing aspect of the problem. The first step in assessing target detection performance is to relate the ground truth to the ATR decisions. Once the ATR decisions have been mapped to ground truth, the second step in the evaluation is to characterize ATR performance. A common approach is to vary the confidence threshold of the ATR and compute the Probability of Detection (PD) and the False Alarm Rate (FAR) associated with each threshold. Varying the threshold, therefore, produces an empirical performance curve relating detection performance to false alarms. Various statistical methods have been developed, largely in the medical imaging literature, to model this curve so that statistical inferences are possible. One approach, based on signal detection theory, generalizes the Receiver Operator Characteristic (ROC) curve. Under this approach, the Free Response Operating Characteristic (FROC) curve models performance for search problems. The FROC model is appropriate when multiple detections are possible and the number of false alarms is unconstrained. The parameterization of the FROC model provides a natural method for characterizing both the operational environment and the ability of the ATR algorithm to detect targets. One parameter of the FROC model indicates the complexity of the clutter by characterizing the propensity for false alarms. The second parameter quantifies the separability between clutter and targets. Thus, the FROC model provides a framework for modeling and predicting ATR performance in multiple environments. This paper presents the FROC model for single sensor data and generalizes the model to handle the fusion case.