The person re-identification task consists in matching person images detected from surveillance cameras with non-overlapping fields of view. Most existing approaches are based on the person's visual appearance. However, one of the main challenges, especially for a large gallery set, is that many people wear very similar clothing. Our proposed approach addresses this issue by exploiting information on the group of persons around the given individual. In this way, possible ambiguities are reduced and the discriminative power for person re-identification is enhanced , since people often walk in groups and even tend to walk alongside strangers. In this paper, we propose to use a deep convolutional neural networks (CNN) to extract group feature representations that are invariant to the relative displacements of individuals within a group. Then we use this group feature representation to perform group association under non-overlapping cameras. Furthermore, we propose a neural network framework to combine the group cue with the single person feature representation to improve the person re-identification performance. We experimentally show that our deep group feature representation achieves a better group association performance than the state-of-the-art methods and that taking into account group context improves the accuracy of the individual re-identification.