Detecting mud hazards is a significant challenge to unmanned ground vehicle (UGV) autonomous off-road navigation. A military UGV stuck in a body of mud during a mission may need to be sacrificed or rescued, both unattractive options. The Jet Propulsion Laboratory is currently developing a daytime mud detection capability under the U.S. Army Research Laboratory Robotics Collaborative Technology Alliances program using UGV-mounted sensors. To perform robust mud detection under all conditions, we expect that multiple sensors will be necessary. A passive mud detection solution is desirable to meet future combat system requirements. To characterize the advantages and disadvantages of candidate passive sensors, outdoor data collections have been performed on wet and dry soil using visible, multispectral (including near-infrared), shortwave infrared, midwave infrared, long-wave infrared, polarization, and stereo sensors. In this paper, we examine the cues for mud detection that each of these sensors provide, along with their deficiencies, and we illustrate localizing detected mud in a world model that can used by a UGV to plan safe paths. We mostly limit our examination to mud detection during the daytime under ideal conditions: isolated wet soil surrounded by dry soil during nominal weather, i.e., no precipitation, calm wind, and near-average temperatures. © 2010 Wiley Periodicals, Inc.