The issue of icing has been around for decades in aviation industry, and while notable improvements have been made in the study of the formation and process of icing, the prediction of icing events is a challenge that has yet to be completely overcome. Low level icing prediction, particularly in complex terrain, has been bumped to the back burner in an attempt to perfect the models created for in-flight icing. However, over the years there have been a number of different, non-model methods used to better refine the variable involved in low-level icing prediction. One of those methods comes through statistical analysis and modeling, particularly through the use of the Classification and Regression Tree (CART) techniques. These techniques examine the statistical significance of each predictor within a data set to determine various decision rules. Those rules in which the overall misclassification error is the smallest are then used to construct a decision tree and can be used to create a forecast for icing events. Using adiabatically adjusted Rapid Update Cycle (RUC) interpolated sounding data these CART techniques are used in this study to examine icing events in the White Mountains of New Hampshire, specifically on the summit of Mount Washington. The Mount Washington Observatory (MWO), which sits on the summit and is manned year around by weather observers, is no stranger to icing occurrences. In fact, the summit sees icing events from October all the way until April, and occasionally even into May. In this study, these events are examined in detail for the October 2010 to April 2011 season, and five CART models generated for icing in general, rime icing, and glaze icing in attempt to create a decision tree or trees with a high predictive accuracy. Also examined in this study for the October 2010 to April 2011 icing season is the Air Weather Service Pamphlet(AWSP) algorithm, a decision tree model currently in use by the Air Force to predict icing events. Producing an icing forecast with this model requires the user to manually work through each branch. Previous work to this end was completed by Stanley et al. 2002, and the goal of this study is to further that work by automating the AWSP using the adiabatically adjusted RUC interpolated sounding data as a test set in an attempt to produce an effective automated forecast tool for icing events in complex terrain.