Machine learning involves a large number of high-dimensional data represented by image processing. PCA, as an effective data dimension reduction method, is often applied for data preprocessing. A tentative inquiry has been made into the principle of K-L data conversion, the specific dimension reduction processing, the co-variance matrix of the high dimensional sample and the method of dimension selection, followed by an accuracy analysis of face recognition based on PCA from ORL face pattern database.