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DoD has defined three levels of data fusion for network centric warfare (NCW). Level 1 data fusion combines data from single or multiple sensors and sources to provide the best estimate of objects and events in the battlespace. Level 2 data fusion focuses on situation assessment. Level 3 data fusion is threat assessment. To facilitate situation assessment, we investigate the problem of jointly classifying and identifying multiple targets in radar sensor networks where the maximum number of categories and the maximum number of targets in each category are obtained a priori based on statistical data. However, the actual number of targets in each category and the actual number of target categories being present at any given time are assumed unknown. It is assumed that a given target belongs to one category and one identification number. The target signals are modeled as zero-mean complex Gaussian processes. We propose a joint multi-target identification and classification (JMIC) algorithm for radar surveillance using cognitive radars. The existing target categories are first classified and then the targets in each category are accordingly identified. Simulation results are presented to evaluate the feasibility and effectiveness of the proposed JMIC algorithm in a query surveillance region.