Electric vehicles (EVs) hold the potential to greatly shape the way the electric power grid functions. As a load, EVs can be managed to prevent overloads on the electric power system. EVs with bidirectional power flow (V2G) can provide a wide range of services, including load balancing, and can be used to increase integration of renewable resources into electric power markets. Realizing the potential of EVs requires more advanced communication than the technology that is in wide use. Common charging standards do not include a means for an EV to send key vehicle characteristics such as maximum charge rate or battery capacity to a charging station and thus to the grid. In response to the need for a means of obtaining vehicle parameters without advanced communication, this paper suggests a mechanism that would allow electric vehicle supply equipment (EVSE) to identify the type (manufacturer, model and year) of the vehicle plugged in, and so learn several of the needed parameters. The approach for identification is proposed based on our measurements of variations in a standard charging protocol implementation across different EV types. We suggest that these variations may uniquely identify, or constitute a “fingerprint” for EV types. This paper describes the tools and methods used to collect data to investigate this proposition. The results of our analysis suggest that the proposed mechanism works well for identifying EV types based only on information available through the interface defined by a common standard for conductive charging. Further work will expand upon these results to develop tools for EVSE to identify the types of EVs connected to charge.