Segmentation of CT images into the various component organs is difficult to perform automatically, because standard methods such as edge tracking, region growing, and simple thresholding do not work. Absolute thresholds are not powerful enough to extract organs, since the gray tones of an organ vary widely depending on the parts, the patient, the CT scanner used, and the setup of the scanner. Edge tracking often fails, because edges around organs are incomplete, and the vagueness of the CT images can mislead most conventional edge detection methods. The nonhomogeneity of organs rules out a region growing approach. Dosimetrists, who trace the boundaries of organs for radiation treatment planning, use their own prior experience with the images and the expected shapes of the organs on various slices to identify organs and their boundaries. The goal of our current work is to develop a knowledge-based recognition system that utilizes knowledge of anatomy and CT imaging. We have developed a system for analyzing CT images of the human abdomen. The system features the use of constraint-based dynamic thresholding, negative-shape constraints to rapidly rule out infeasible segmentation, and progressive landmarking that takes advantage of the different degrees of certainty of successful identification of each organ. The results of a series of initial tests on our training data of 100 images from five patients indicate that the knowledge-based approach is promising.