Infrared spectroscopic imaging is a promising intraoperative tool which enables rapid, on-site diagnosis of brain tumors during neurosurgery. A classification model was recently developed using infrared spectroscopic images from thin tissue sections to grade malignant gliomas, the most frequent class of primary brain tumor. In this study the model was applied to 54 specimens from six patients with inhomogeneous gliomas composed of regions with different tumor density and morphology. The resection was controlled using neuronavigation which transfers the findings obtained by preoperative magnetic resonance imaging (MRI) into the operating field. For comparison, all specimens were independently evaluated by histopathology after hematoxylin and eosin staining. The infrared-derived grading agreed with histopathology and MRI findings for almost all specimens. With regard to histopathological assessment, sensitivities of 100% (22/22) and 93.1% (27/29) and specificities of 96.9% (31/32) and 88.0% (22/25) were achieved, depending on whether the classification was based on the predominant or maximal tumor grade, respectively, in the specimen. Altogether, in 98% (53/54) of all specimens the decision to continue or not continue tumor resection could have been made according to the infrared spectroscopic classification. This retrospective study clearly demonstrates that infrared spectroscopic imaging may help to define tumor margins intraoperatively and to detect high-grade tumor residues for achieving more radical tumor resection. [Figure not available: see fulltext.]