Image classification is an essential problem for content based image retrieval and image processing. Visual properties can be extracted from images in the form of MPEG-7 descriptors. Statistical methods can use these properties as features and be used to derive an effective method of classifying images by evaluating a minimal number of properties used in the MPEG-7 descriptor. Classification by artist, artistic movement, and indoor/outdoor setting is examined using J48, J48 graft, best first, functional, and least absolute deviation tree algorithms. An improved accuracy of 11% in classification of artist and 17% in classification of artistic movement over previous work is achieved using functional trees. In addition classification by indoor/outdoor setting shows that the method can be applied to new categories. We present an analysis of generated decision trees that shows edge histogram information is most prominent in classification of artists and artistic movements, while scalable color information is most useful for classification of indoor/outdoor setting.