Abstract In this paper, we propose a consistent-aware deep learning (CADL) approach for person re-identification in a camera network. Unlike most existing person re-identification methods which identify whether two pedestrian images are from the same person or not, our approach aims to obtain the maximal correct matches for the whole camera network. Different from recently proposed camera network based reidentification methods which only consider the consistent information in the matching stage to obtain a globally optimal association, we exploit such consistent-aware information under a deep learning framework where both feature representation and image matching are automatically learned. Specifically, we reach the globally optimal solution and balance the performance between different cameras by optimizing the similarity and data association iteratively with certain consistent constraints. Experimental results show that our method obtains significant performance improvement and outperforms the state-of-the-art methods by large margins.