This paper proposes a simple color image coding method using Principal Component Analysis (PCA) in the segmented color areas. A color image is segmented into different object areas with clustered color distributions. The chrominance a* and b* values in CIELAB space are observed to be strongly correlated with luminance L* value in the object areas. After the segmentation, each object area is characterized by PCA. The segmented object areas are indexed by the class number which is greatly com-pressed by the conventional loss-less coding. The coded class number is transmitted with L* image and the PCA parameters. PCA parameters are also compressed, because they are transmitted not by every pixel but by one set for each object area. The (a*, b*) values of each pixel are predicted by the projection of L* onto chromatic plane along to the first PC axis given by eigen vectors and are approximately restored from L* value. L* image can be compressed by the conventional lossive coding method such as JPEG or Wavelet. Finally, the full color image is reproduced by combining the luminance L* with (a*, b*). The paper discusses the coding efficiency and the image quality changing with the class number.