In many surveillance systems there is a requirement to determine whether a given person of interest has already been observed over a network of cameras. This paper presents two approaches for this person re-identification problem. In general the human appearance obtained in one camera is usually different from the ones obtained in another camera. In order to re-identify people the human signature should handle difference in illumination, pose and camera parameters. Our appearance models are based on haar-like features and dominant color descriptors. The AdaBoost scheme is applied to both descriptors to achieve the most invariant and discriminative signature. The methods are evaluated using benchmark video sequences with different camera views where people are automatically detected using Histograms of Oriented Gradients (HOG). The reidentification performance is presented using the cumulative matching characteristic (CMC) curve.