In this paper, we propose a new classification technique based on the minimum component analysis (MCA) instead of the traditional principal components analysis (PCA). Most existing classification techniques based on PCA like to represent a class by its principal component. However, the principal component is not always the best choice since it has a high possibility for a class to overlap with other classes in the principal component direction. The new minimum component eigen-vector based classification technique overcomes this disadvantage by representing a class with its minimum component. In addition, a minimum likelihood decision rule is employed instead of maximum likelihood decision rule. Good performance of our technique is verified by experimental results on Kennedy Space Center (KSC) TM images.