Early research in terrain estimation has relied on forward looking sensors and used limited learning [7]. Monocular and stereo cameras have been the most common sensors used for terrain estimation from a distance [8], followed by lidars and radars [9], which have been often proposed in terrestrial applications [10; 11]. The use of exteroceptive sensing leads to the generation of a local digital elevation model (DEM) to obtain and maintain a discrete traversability map. The appearance (e.g., texture, color) of different terrain patches can provide important clues to analyze the surrounding terrain [12]. However, observation of a given terrain from a distance does not provide any information about its impact on the vehicle mobility. It is known that off-road traversability largely depends on the interaction between the robot and the terrain [13]. Dynamic ill-effects including wheel sinkage, slippage and rolling resistance are the result of this complex interplay. For example, ground can be considered drivable based on the geometric elevation map. Yet, the robot can incur serious risks if this terrain offers low traction properties due to high slippage and consequent lack of progression as explained in [14]. An extensive discussion on methods for slippage estimation in planetary rovers can be found in [15], whereas the impact of the irregularity and deformability of the traversed surface on the robot's dynamic response are investigated in [16]. Therefore, recently, methods for terrain estimation have been also presented that use proprioceptive sensing [17; 18]. The envisaged idea is that terrain properties can be obtained directly by the rover wheels that serve as tactile sensors. Proprioceptive signals are modulated by the vehicle-terrain interaction and they contain substantial information, which can help to characterize terrain. In addition, learning approaches have been introduced in order to make intelligent autonomous robots adaptive to the site-specific environment [19]. Natural terrains represent a challenging scenario due to variability in surface and lighting conditions, lack of structure, no prior information, and in which learning approaches have proved to be more appropriate than expert rule-based or heuristic strategies. For example, the vertical acceleration was used as the main sensory input to train classifiers based on different learning algorithms, including AdaBoost [20], neural network [21], and Cubature Kalman filtering [22]. Methods that attempt to directly measure some important terrain parameters such as friction angle and cohesion have been proposed using a linear least squares approximation of the classical terramechanics theory [23] or via a Bayesian procedure to deal effectively with the presence of uncertainty [24]. The general learning approach includes data gathering pertaining to the wheel-terrain interaction followed by a mapping stage of proprioceptive data with the corresponding terrain. This functional relationship can help addressing various issues: (a) difficulty in creating a physics-based terrain model due to the large number of variables involved, (b) the mapping from proprioceptive input to a mechanical terrain property is an extremely complicated function, which does not have a known analytical form or a physical model and one possible way to observe it and learn about it is via training examples, (c) a learning approach promotes adaptability of the vehicle's behavior. This paper presents an approach for terrain identification by gathering important information pertaining to the mechanics of vehicle-environment interaction. The underlying assumption is that characteristic traits of the supporting surface can be extracted using vehicle wheels as \"tactile\" sensors that generate signals modulated by the physical wheel-soil contact. First, the main features that hold the highest discriminative power for terrain identification are studied. They form a feature space upon which a terrain classifier can be built via support vector machine (SVM).