Climate change has become the top concern among scientists as the biggest existential threat to humans. The effects of this change continue to be felt across the globe as extreme droughts, stronger hurricanes, frequent flooding, and other catastrophic environmental disasters become more frequent. The attention given to climate studies such as this can no longer be undervalued. The methods used in this analysis included principal components analysis, artificial neural network-based SOM (Self Organizing Map) clustering, cluster validation/evaluation, trend analysis and correlative analysis. The SOM-based classification was applied to data sets of convective available potential energy (CAPE), convective inhibition (CIN), storm relative helicity (SRH), and 2m dew point to create a discrete set of 20 tornado favorable environments (TFEs).The classification produced strong correlations between the spatial distributions of tornado days and the aforementioned atmospheric patterns.