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Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key (BSK) is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent in-spection process is divided into three stages:first, the region-of-interest (ROI) is segmented from the source image by an im-proved spatial pyramid matching scheme based on multi-scale census transform (MSCT). To localize the BSK in the ROI, cen-sus transform (CT) on gradient images is developed in the second stage. Then gradient encoding histogram (GEH) features and linear support vector machines (SVMs) are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2%and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.