An object-oriented color matching strategy depending on the image contents is proposed. Pictorial color image is segmented into different object areas with clustered color distributions. Euclidian or Mahalanobis color distance measures, and Bayesian decision rule based on maximum likelihood theory, are introduced to the segmentation. After the objects' segmentation, each clustered pixels are projected onto principal component space by Hotelling transform and the color mappings are performed for the principal components to be matched in between the individual objects of original and printed images.