Matching specific persons across scenes, known as person re-identification, is an important yet unsolved computer vision problem. Feature representation and metric learning are two fundamental factors in person re-identification. However, current person re-identification methods, which use single handcrafted feature with corresponding metric, could be not powerful enough when facing illumination, viewpoint and pose variations. Thus it inevitably produces suboptimal ranking lists. In this paper, we propose incorporating multiple features with metrics to build weak learners, and aggregate the base ranking lists by AdaBoost Ranking. Experiments on two commonly used datasets, VIPeR and CUHK01, show that our proposed approach greatly improves recognition rates over the state-of-the-art methods.