The Markov random field (MRF) formation allows independence over small pixel neighborhoods suitable for SIMD implementation. The equivalence between the Gibbs distribution over global configurations and MRF allows description of the problem as maximizing a probability or, equivalently, minimizing an energy function (EF). The EF is a convenient device for integrating 'votes' from disparate, preprocessed features--mean intensity, variance, moments, etc. Contributions from each feature are simply weighted and summed. The EF is flexible and can be easily modified to capture a priori beliefs about the distribution of the configuration space, and still remain theoretically sound. A unique formulation of the EF is given. Notably, a deterministic edge finder contributes to the EF. Weights are independently assigned to each feature's report (indicators). Simulated annealing is the theoretical mechanism which guarantees convergence in distribution to a global minimum. Because the number of iterations is an exponential function of time, the authors depart from theory and implement a fast, heuristic 'cooling' schedule. A videotape of results on simulated FLIR imagery demonstrates real-time update over the entire image. Actual convergence is still too slow for real-time use (O(1 min.)), but the quality of results compares favorably with other region labeling schemes.