The work aims to propose a feature selection method of printing mark images dataset based on max-relevance and min-redundancy in view of that the model of printing registration recognition cannot accurately identify the printing misregistration images due to the imbalanced training set. The multi-dimensional features of printing mark images were extracted, and the correlation between features and printing registration/misregistration and the redundancy between features were calculated. The objective function of feature selection was determined, and the incremental search method was used to find the optimal feature and add the optimal feature to the feature subset, which realized the feature selection of imbalanced printing mark images. The proposed method achieved 3 evaluation indicators of imbalanced data classification, 0.9900 of A, 0.9400 of R, and 0.9466 of Gmean. The proposed method outperforms the untreated method, the PCA method, the Relief method and the NCA method on the identification of imbalanced printing mark images in the experiment.