We propose two variable selection methods using signomial classification. We attempt to select, among a set of the input variables, the variables that lead to the best performance of the classifier. One method repeatedly removes variables based on backward selection, whereas the second method directly selects a set of variables by solving an optimization problem. The proposed methods conduct variable selection considering nonlinear interactions of variables and obtain a signomial classifier with the selected variables. Computational results show that the proposed methods more effectively selects desirable variables for predicting output and provide the classifiers with better or comparable test error rates, as compared with existing methods.