Pedestrian recognition is one of the key technologies for driving assistance systems. This paper proposes a pedestrian recognition system using a high-definition LIDAR and a vision sensor to achieve high performance in various conditions. Pedestrian candidates are extracted from two sensors in parallel by the SVM-based classifiers. In particular, the region-of-interests in the image processing is set by information about objects derived from LIDAR to reduce false positives as well as computational burden. All candidates are integrated by their likelihood calculated from their classification scores. A quantitative evaluation in a road environment confirms the effectiveness of the proposed system.