By the end of 2012, the National Weather Service will complete upgrades the current network of WSR-88D radars to include polarimetric capability. In principle, this upgrade allows meteorologists to discern the nature of particles that scatter microwave energy back to the radar. In particular, this upgrade allows for more accurate rainfall estimation, classification of hydrometeor type, and identification of returns caused by birds, insects, and ground clutter (including anomalous propagation). Because the National Weather Service is most interested in weather at and near the surface, using polarimetric radar to diagnose precipitation type at the surface is vitally important. This lead-off paper will introduce the polarimetric radar, the data it generates, how observations near the surface were obtained, and some the of the inherent limitations of both the radar and validation data. We will also discuss the current state of existing classification algorithms, how they have been developed, and their performance in this application. We will explain why we have chosen the Peirce Skill Score as the metric by which to judge entries in the competition. Finally, we will show examples of some of the data along with the true nature of potential inputs to any classification scheme.