Recently abnormality detection in musculoskeletal radio-graphs has attracted many attentions. For abnormality detection, it is crucial to locate the most important area in the musculoskeletal radiographs. To achieve this goal, we propose a key area localization mechanism (KALM) for abnormality detection for the first time in this paper. The proposed KALM explicitly defines the process of selecting the most important area from the whole image with using only image-level label. Based on KALM, we further present a joint global and local feature representation strategy for abnormality detection which takes as input both the entire image and the selected local area. The experimental results based on several classical convolutional neural network (CNN) architectures of MURA, the largest abnormality detection dataset of musculoskeletal radiographs, demonstrate the effectiveness of our KALM.