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Over the past few years, automatic target recognition (ATR) has emerged as an essential image analysis tool to identify objects from temporally and spatially disjoint possibly noisy image data. For many current applications, ATR is performed by unmanned aerial vehicles (UAVs) that fly within a reconnaissance area to collect image data through sensors and upload the data to a central ground control station for analyzing and identifying potential targets. The centralized approach to ATR introduces several problems, including scalability with the number of UAVs, network delays in communicating with the central location, and the susceptibility of the system to malicious attacks on the central location. These challenges can be addressed by using a distributed system for performing ATR. In this paper, we describe a multiagent-based prototype system that uses swarming techniques inspired from insect colonies to perform ATR using UAVs in a distributed manner within simulated scenarios. We assume that UAVs are constrained in the resources available onboard and in their capabilities for performing ATR due to payload limitations. Our focus in this paper is on the coordination aspects between UAVs to efficiently decide how they are to act by using a swarming mechanism. We describe algorithms for the different operations performed by the UAVs in the system and for different swarming strategies, which are embedded within software agents located on the UAVs. We provide empirical simulations of our system within a simulated area of interest to determine its behavior in different scenarios with varying operational constraints. Our experimental results indicate that swarming strategies for distributed ATR perform favorably compared with centralized ATR strategies.