PURPOSE: To simply learn a new category by producing a mapping matrix and recognizing dictionary for extraction of features of a discriminating method and a feature vector of each category via the K-L evolution of a single time.

CONSTITUTION: An average initial vector of the learning character categories is produced from an initial vector where the feature values detected out of the pictures of learning categories. An inter-category covariance matrix is obtained from said average initial vector and the average initial vector of the categories registered before learning. Then an inter-normalized category covariance matrix is obtained from the product obtained between a normalized mapping matrix produced previously from an intra category covariance matrix and the inter-category covariance matrix. Then the proper vectors obtained by applying the K-L evolution to the inter-normalized category covariance matrix are arranged for production of an inter-category emphasis matrix which emphasizes the difference of categories. Then a matrix obtained from the product of the normalized mapping matrix and the inter-category emphasis matrix is used as a mapping matrix for decision/analysis. While a vector obtained from the product of the average initial vector of each category and the mapping matrix is used as a feature vector of a dictionary. Thus the learning is possible in a simple process.