As a molecular probe of tissue composition, infrared spectroscopic imaging serves as an adjunct to histopathology in detecting and diagnosing disease. In the past it was demonstrated that the IR spectra of brain tumors can be discriminated from one another according to their grade of malignancy. Although classification success rates up to 93% were observed one problem consists in the variation of the models depending on the number of samples used for the development of the classification model. In order to open the path for clinical trials the classification has to be validated. A series of classification models were built using a k-fold cross validation scheme and the classification predictions from the various models were combined to provide an aggregated prediction. The validation highlights instabilities in the models, error rates, sensitivity as well as specificity of the classification and allows the determination of confidence intervals. Better classification models could be achieved by an aggregated prediction. The validation shows that brain tumors can be classified by infrared spectroscopy and the grade of malignancy corresponds reasonably to the histopathological assignment.