Terrain identification is a key enabling ability for generating terrain adaptive behaviors that assist both robot planning and motor control. This paper considers running legged robots from the RHex family) which the military plans to use in the field to assist troops in reconnaissance tasks. Important terrain adaptive behaviors include the selection of gaits) modulation of leg stiffness) and alteration of steering control laws that minimize slippage) maximize speed and/or reduce energy consumption. These terrain adaptive behaviors can be enabled by a terrain identification methodology that combines proprioceptive sensors already available in RHex-type robots. The proposed classification approach is based on the characteristic frequency signatures of data from leg observers) which combine current sensing with a dynamic model of the leg motion. The paper analyzes the classification accuracy obtained using both a single leg and groups of legs (through a voting scheme) on different terrains such as vinyl) asphalt) grass) and pebbles. Additionally) it presents a terrain classifier that works across various gait speeds and in fact almost as good as an overly specialized classifier.