Many autonomous ground vehicle (AGV) missions, such as those related to agricultural applications, search and rescue, or reconnaissance and surveillance, require the vehicle to operate in difficult outdoor terrains such as sand, mud, or snow. To ensure the safety and performance of AGVs on these terrains, a terrain-dependent driving and control system can be implemented. A key first step in implementing this system is autonomous terrain classification. It has recently been shown that the magnitude of the spatial frequency response of the terrain is an effective terrain signature. Furthermore, since the spatial frequency response is mapped by an AGV’s vibration transfer function to the frequency response of the vibration measurements, the magnitude of the latter frequency responses also serve as a terrain signature. Hence, this paper focuses on terrain classification using vibration measurements. Classification is performed using a probabilistic neural network, which can be implemented online at relatively high computational speeds. The algorithm is applied experimentally to both an ATRV-Jr and an eXperimental Unmanned Vehicle (XUV) at multiple speeds. The experimental results show the efficacy of the proposed approach.