This paper addresses dynamic data-driven signature detection in mobile robots. The core concept of the paper is built upon the principles of Symbolic Dynamic Filtering (SDF) that has been recently reported in literature for extraction of relevant information (i.e., features) in complex dynamical systems. The objective here is to identify the robot behavior in real time as accurately as possible. Two different approaches to classifier design are presented in the paper; the first one is Bayesian and the second is based on measures of formal languages. The proposed methods have been experimentally validated on a networked robotic testbed to detect and identify the type and motion profile of the robots under consideration.