SVM is a non-linear classifier in the input space and, accordingly, the use of linear statistics to screen the predictor pool a priori, may not be logically consistent. In this research, the impact of removing individual predictors is examined on the training and testing errors. The model was trained on a 50 percent tornado, 50 percent non-tornado ratio and was tested on a 2 percent tornado, 98 percent non-tornado ratio. Results were encouraging as exclusion of specific variables had a notable impact on the ability to distinguish accurately the tornadic from the non-tornadic circulations when viewed from misclassification rates, POD, FAR, and Heidke skill. A key finding is that inclusion of the current month number (1= January, 2 = February, …) in the testing data in addition to a subset of MDA predictors used in SVM is the most accurate set of features tested. The methodology used for feature selection outperforms SVM based on the MDA alone, achieving a Heidke skill of 0.84 with a POD of 0.82 and a FAR of 0.14.