Tripitaka Koreana is a collection of over 80,000 Buddhist texts carved on wooden blocks. In this study, we investigated whether six hardwood species used as blocks could be recognized by image recognition. An image data set comprising stereograms in transverse section was acquired at 10x magnification. After auto-rotation, cropping, and filtering processes, the data set was analyzed by an image recognition system, which comprised a gray-level co-occurrence matrix method for feature extraction and a weighted neighbor distance algorithm for classification. The estimated accuracy obtained by leave-one-out cross-validation was up to 100% after optimizing the pretreatments and parameters, thereby indicating that the proposed system may be useful for the non-destructive analysis of all wooden carvings. We also examined the specific anatomical features represented by textures in the images. Many of the texture features were apparently related to the density of vessels, and others were associated with the ray intervals. However, some anatomical features that are helpful for visual inspection were ignored by the proposed system despite its perfect accuracy. In addition to the high analytical accuracy of this system, a deeper understanding of the relationships between the calculated and actual features is essential for the further development of automated recognition.