A new approach is presented to distinguish cancerous from normal brain tissue via linear discriminant analysis of Fourier transform infrared (FTIR) spectra. FTIR microspectroscopy was used to map various thin-section tumour samples with different malignancy grades (grades II-VI) and non-tumour samples obtained from various patients by surgical removal. Spectral analysis revealed features characteristic of tumors with increasing malignancy. A genetic region selection algorithm combined with linear discriminant analysis was used to derive classifiers distinguishing among spectra of control tissue, astrocytoma grade II, astrocytoma grade III and glioblastoma grade IV. Employing the World Health Organization histopathological diagnostic scheme as the gold standard, the spectra were classified with a success rate of ∼ 85 %. These results demonstrate the potential of the combination of FTIR spectroscopy and pattern recognition routines in providing a more objective method for brain turnout grading and diagnosis.