Automatic target recognition (ATR) algorithms have offered the promise of recognizing items of military importance over the past 20 years. It is the experience of the authors that greater ATR success would be possible if the ATR were used to 'aid' the human operator instead of automatically 'direct' the operator. ATRs have failed not due to their probability of detection versus false alarm rate, but to neglect of the human component. ATRs are designed to improve overall throughput by relieving the human operator of the need to perform repetitive tasks like scanning vast quantities of imagery for possible targets. ATRs are typically inserted prior to the operator and provide cues, which are then accepted or rejected. From our experience at three field exercises and a current operational deployment to the Bosnian theater, this is not the best way to get total system performance. The human operator makes decisions based on learning, history of past events, and surrounding contextual information. Loss of these factors by providing imagery, latent with symbolic cues on top of the original imagery, actually increases the workload of the operator. This paper covers the lessons learned from the field demonstrations and the operational deployment. The reconnaissance and intelligence community's primary use of an ATR should be to establish prioritized cues of potential targets for an operator to 'pull' from and to be able to 'send' targets identified by the operator for a 'second opinion.' The Army and Air Force are modifying their exploitation workstations over the next 18 months to use ATRs, which operate in this fashion. This will be the future architecture that ATRs for the reconnaissance and intelligence community should integrate into.