In the late 1990s, ARL:UT developed a real-time system capable of detecting and classifying a variety of vehicles using a single 3-axis seismic sensor. At that time, the detection and classification software was designed to classify vehicles for a variety of features, including the number of cylinders in the engine, number of axles, engine type, traction mechanism, and relative weight. Using a single 3-axis seismic sensor, buried approximately 3 feet deep, the demonstration system showed that it was possible to detect and classify a single time-critical target, moving with constant bearing and speed. Since then, modifications have been made to the detection and classification software in support of a transition of the algorithms into unattended MASINT sensors (UMS). This paper describes the next evolution of the detection and classification system, which focuses on generating highly accurate traction mechanism and relative weight classifications. The current build of the software has proven capable of detecting vehicles and generating high confidence classifications for a wide range of vehicles and test scenarios, and performs well for both buried and hand-emplaced sensors. Included in the paper is a description of the upgrades made to the classification algorithms, event switching, and graphical user interface (GUI), an outline of the processing and analysis of seismic data, and a summary of the software performance for a wider range of vehicles, test scenarios, and sensor configurations.