In this paper, different types of machine learning classifiers, such as support vector machines (SVMs), artificial neural networks (ANNs), and linear discriminant analysis (LDA), are applied for tornado detection. All methods are used to predict which storm-scale circulations yield tornadoes based on the radar derived Mesocyclone Detection Algorithm (MDA) attributes and a month attribute. The incorporation of near-storm environment (NSE) attributes as inputs to the classifiers is investigated. The sensitivity analysis for each classifier on different ratios between tornadic and non-tornadic observations in the data sets is performed. The computational results show that SVMs have a higher performance compared to ANNs and LDA.