A rule-based interpretation system for images consisting of signals (such as seismic images) is presented in this paper. A test run of the interpreter on a piece of real seismic data shows that it can successfully segment the seismic image into regions of common signal character. The system consists of two substructures: a texture analyzer and an intelligent interpreter. The texture analyzer adapts the "texture energy measurement" method developed by Laws to extract discriminant features from the texture-like signal image. The major function of the analyzer is to assign a vector of initial certainty factors (CFs) to each texel in the image based on the extracted feature measures. The elements of the CF vector essentially correspond to the degree of membership of the texel to each of the texture regions in the image. The intelligent interpreter, which is the rule-based system, is made up of a knowledge database, a reasoning engine and a parallel region growing controller. Whenever a growing region requests to classify one of its boundary texels, a fact list is formed carrying information about the texel and its neighborhood. The interpreter takes the fact list and searches in the knowledge database to determine if any rules can be exercised. In the case of a seismic image, the rules are mostly geologic in nature. After a sequence of executions of rules and manipulations of CF numbers, a final CF vector emerges. If this vector favors the requesting region by having its corresponding component element exceed a preset threshold, the texel is classified to this texture class and merged into the requesting region. A noticeable advantage of the intelligent interpreter over other conventional classification techniques is its capability of patching small areas in the image for which the original data apparently do not provide enough discriminant information. An example illustrating these results on real seismic data from the Gulf of Mexico is presented.