This paper describes a tactile probe designed for surface identification, in a context of all-terrain low-velocity mobile robotics. The proposed tactile probe is made of a small metallic rod with a single-axis accelerometer attached near its tip. Surface identification is based on analyzing acceleration patterns induced at the tip of this mechanically robust tactile probe, while it is passively dragged along a surface. A training data set was collected over ten different indoor and outdoor surfaces. Classification results for an artificial neural network were positive, with 89.9 % and 94.6 % success rate for 1 and 4 second time-windows of data, respectively. We also demonstrated that the same tactile probe can be used for unsupervised learning of terrains. For 1 second time-windows of data, the classification success rate was only reduced to 74.1 %. Finally, a blind mobile robot, performing real-time classification of surfaces, demonstrated the feasibility of this tactile probe as a guidance mechanism.