Advances in segmentation algorithm design can be guided by the development of uniform methods for segmentation accuracy measurement. A representative set of approaches to segmentation accuracy measurement is presented, and performance is demonstrated on a prototype set of segmented imagery. Unlike the evaluation of the detection and classification phases of the automatic target recognition (ATR) process, segmentation evaluation remains an ill-defined problem in the absence of an absolute, correct segmentation reference. A dynamic assessment of image truth from multiple sources can reduce the uncertainty of the target reference. We describe two approaches to the measurement of segmentation accuracy that make use of internal characteristics of the image in the target reference. These measures begin to demonstrate that the truthing procedure in real-time systems is not necessarily a singlepass reference to a prior model of image truth, but an evolving set of criteria utilizing information from the unprocessed image and from intermediate stages of the segmentation process. Sample measures based on an independent determination of image truth, i.e., hand segmentation of the target regions by the human operator or the manipulation of models of expected target configuration, are also presented. The form of the sampIe measures can be optimized and integrated into a dynamic truthing process to achieve real-time management of the segmentation phase in ATR systems.