This paper presents an automatic selection method of representative images for deterioration diagnosis of steel towers. Although representative images have been used in deterioration diagnosis for reducing variations in diagnostic results, these images have been selected manually by experienced inspectors. Thus, it is desired that representative images will be selected automatically and updated adaptively. We propose the automatic representative image selection method employing a Machine learning in this paper. Furthermore, we examine the effect of representative images selected by our method through the experiment.