Chest radiography is one of the most widely used techniques in diagnostic imaging. It makes up at least one third of all conventional diagnostic radiographic procedures in hospitals. However, in both film-screen and computed radiography, images are often digitized with the view and orientation unknown or mislabeled, which causes inefficiency in displaying them in the picture archive and communication system (PACS). Hence, the goal of this work is to provide a robust, efficient, and automatic hanging protocol for chest radiographs. To achieve it, the method star ts with recognition by extracting a set of distinctive features from chest radiographs. Next, a well-defined probabilistic classifier is used to train and classify the radiographs. Identifying the orientation of the radiographs is performed by an efficient algorithm which locates the neck, heart, and abdomen positions in radiographs. The initial experiment was performed on radiographs collected from daily routine chest exams in hospitals, and it has shown promising results.