This paper proposes a simple color image coding method using Principal Component Analysis (PCA) in the segmented color areas. A color image is segmented in the CIELAB color space based on the human perception. In the clustered color area, the chroma a* and b* look to be strongly correlated with lightness L*, and this tendency becomes remarkable by the segmentation. After the segmentation, each cluster is characterized by PCA. The segmented image is labeled with the class number. Since the class number is denoted by an integer value with narrow range but represents the segment color, it is compressed by the conventional loss-less coding. The coded class number is transmitted with the first Principal Component (PC) parameters. Because one set of PC parameters (eigen vector and centroid vector) is transmitted corresponding to each class, it takes small memory capacity. The chroma values of each pixel are approximately reproduced by the projection of first PC onto a*-b* plane along the first PC axis defined by eigen vectors. The first PC value on eigen vector axis mainly carries the lightness information with high resolution. It must be transmitted every pixel and compressed by the conventional Wavelet coding. After restoring the lightness L* from first PC, the full color image is reproduced by combining the estimated chroma components (a*, b*). This paper discusses the coding efficiency and the color reproduction error in relation to the segmented class number.