In this study, we propose an efficient method for classifying images of Ise-Katagami, a traditional Japanese paper stencil used for dyeing kimono fabric, by leveraging principal component analysis (PCA). Ise-Katagami has been produced in the Ise region of Japan for centuries, and a vast number of digital images have been preserved. However, issues related to copyright and ownership have hindered the application of conventional deep learning or transfer learning methods. To address this challenge, we introduce a simple yet effective classification approach based solely on PCA. Unlike deep learning models, our method does not require pre-training, eliminating the need for a large dataset while enabling rapid classification. Furthermore, compared to existing techniques that rely on image distance calculations, our approach offers both higher efficiency and improved classification accuracy.