Vehicle re-identification (re-ID) has numerous applications in real life, such as video surveillance, information retrieval, and public security. However, it suffers from the misalignment of vehicles, which is a critical problem caused by inaccurate detection and different views. This paper proposes a novel network named Part Alignment Network (PAN) which can properly handle the misalignment of vehicles. It is therefore adapted to the vehicle re-ID task, avoiding the use of any additional pre-processing steps such as the annotation of vehicle key points and part segmentation by hand. In PAN, cross-correlation is adopted to the alignment of vehicle parts. Then, an effective network architecture is designed to extract the discriminative aligned features. By combining complementary aligned features and original features, more robust feature representations are learned. To show the effectiveness of PAN, this paper conducts experiments on three vehicle re-ID databases (VD1, VD2, and VehicleID), on which it improves the current state-of-the-art performance.