The work aims to study the method of printing registration recognition which consists of the multi-dimension feature extraction, fusion and dimension reduction of the printing mark images, with respect to the problem of being unable to accurately represent the printing mark registration state with the single-style features. The gray level co-occurrence matrix, the Tamura texture feature, the gray difference statistical feature and the gray gradient co-occurrence matrix of the printing mark images were extracted to represent their texture. Then, the principal component analysis was carried out to reduce the dimension of the fused multi-dimensional features to obtain the principal features. The printing mark images with the principal featrues were divided into two sections: the training set and the testing set. The training set was learned by the support vector machine (SVM) model, so as to determine the parameters of this model, and the performance of this model was verified on the testing set. The proposed method achieved the recognition accuracy of 99% on the testing set, the SVM model training time of 1.9327 s on the training set, the SVM model recognition time of 0.0307 s on the testing set, and the model's total time (sum of training time and recognition time) of 1.9634 s. The proposed method outperforms the methods of the single-style features in terms of the recognition accuracy. Meanwhile, without decreasing the recognition accuracy, the proposed method is also better than the Non-PCA dimension reduction method in terms of the training time, the testing time and the total time.