This paper presents local area enhancement of the segmented color image obtained from the multi-spectral image clustering by using FCM (fuzzy c-means). In case, the multi-spectral images, which have the number of bands more than that of 3, must decrease the data volume to remain the number of bands of 3 in order to correspond with the meaning of red, green, and blue images. PCA (Principal Components Analysis) is then used to transform original multi-spectral images into PCA images. The first three components having information more than that of original images of 95% is assigned as red, green and blue images, namely RGB color image. FCM clustering apply to RGB color image, separately. This method is called the PCA-FCM technique being the multi-spectral image clustering. By applying such technique, the result images consisted of red, green, and blue images separately are the segmented images. By histogram equalization algorithm, the result of local area enhancement based on a number of clusters as the segmented image can solve effect of intensity saturation from global area enhancement and the perceptibility of color image is clearly improved.