Market segmentation is an important area of marketing. In this field, researchers use clustering algorithms to divide customers into homogeneous groups. Traditionally, these groups are formed on the basis of survey data. In these surveys, the test persons often have to answer a variety of questions. With the increasing amount of digitalization and improved technical capabilities, new databases are now available for this purpose. For example, potential customers might provide photos that describe their activities, interests, or opinions. In the area of content-based image retrieval (CBIR) there are various methods that currently exist to analyze the similarity of such photos, e.g., using distributional descriptors of colors, textures, or shapes. In this paper we discuss which dissimilarity measures could be used to segment photos by hierarchical clustering on the basis of color. For this purpose we analyzed 2,100 images concerning three color spaces RGB, HSV and CIE L*a*b* using different distance measures as the basis for hierarchical clustering.