As a molecular probe of tissue composition, infrared spectroscopic imaging can potentially serve as an adjunct to histopathology in detecting and diagnosing disease. This study demonstrates that human gliomas are distinguishable from control tissue on the basis of IR image used in combination with chemometric imaging processing. Using an iterative two-step algorithm - comprised of a linear discriminant analysis guided genetic optimal spectral region selection - tissue types can be discriminated from one another thus providing insight into the malignancy grade of the tissue. 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 of the aggregated model reveals an improvement in the classification success rate to 64%.