For describing and analyzing digital images of paintings we propose a model to serve as the basis for an interactive image retrieval system. The model defines two types of features: palette and canvas features. Palette features are those related to the set of colors in a painting while canvas features relate to the frequency and spatial distribution of those colors. The image retrieval system differs from previous retrieval systems for paintings in that it does not rely on image or color segmentation. The features specified in the model can be extracted from any image and stored in a database with other control information. Users select a sample image and the system returns the ten closest images as determined by calculating the Euclidean distance between feature sets. The system was tested with an initial dataset of 100 images (training set) and 90 sample images (testing set). In 81 percent of test cases, the system retrieved at least one painting by the same artist suggesting that the model is sufficient for the interactive classification of paintings by artist. Future studies aim to expand and refine the model for the classification of artwork according to artist and period style.