Knowledge of the physical properties of terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Two classification algorithms for color, texture, and range features are presented based on maximum likelihood estimation and support vector machines. In addition, a classification method based on vibration features derived from rover wheel-terrain interaction is briefly described. Two techniques for merging the results of these "low-level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performance of these algorithms is studied using images from NASA's Mars Exploration Rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual and tactile features.