The paper aims to research a single classification model recognition method for imbalanced printing mark images in registration state to solve the problem that the binary classifier model constructed from the imbalanced training set of printing mark images cannot recognize the minority printing misregistration images accurately. The support vector data description (SVDD) method was proposed to recognize the majority registration images and the minority misregistration images accurately. The majority printing registration images wereused for training of SVDD, and the SVDD model wasconstructed. The grid optimization method and cross validation method wereused to determine the optimal parameters and of the model. The registration of the printing mark images was identified with the model. The proposed SVDD methodachieved 0.9500 of overall recognition accuracya, and 0.9513 of geometric mean of the recognition accuracy Gmean. The overall recognition accuracy a and the geometric mean of the recognition accuracy Gmean obtained by the proposed method are superior to that of other methods in the experiment.