The aim of this work was to study the rapid and highly accurate recognition method for printing registration based on machine vision. Gray level co-occurrence matrix of the printing marks image was extracted to represent its texture features. Adaboost classifier was used to recognize printing marks images, to check the accuracy of printing registration. 8-dimentioal texture vectors including the means and standard deviations of energy, entropy, moment of inertia and correlation in the printing marks images were extracted. To compare the classification performance of different types of classifiers, the accuracy and runtime of classifying these 8-dimentioal vectors were obtained using Adaboost, K-Nearest Neighbor, Naivebayes, Support Vector Machine, Fisher and Decision Tree. The recognition rate of 97.5 % and the classification runtime of 0.022 377 s could be achieved using the proposed method, superior to other classification methods.