Introduction MR-based differentiation between low- and high-grade gliomas is predominately based on contrastenhanced T1-weighted images (CE-T1w). However, functional MR sequences as perfusion- and diffusion-weighted sequences can provide additional information on tumor grade. Here, we tested the potential of a recently developed similarity search based method that integrates information of CE-T1w and perfusion maps for non-invasive MR-based glioma grading. Methods We prospectively included 37 untreated glioma patients (23 grade I/II, 14 grade III gliomas), in whom 3T MRI with FLAIR, pre- and post-contrast T1-weighted, and perfusion sequences was performed. Cerebral blood volume, erebral blood flow, and mean transit time maps as well as CE-T1w images were used as input for the similarity search. Data sets were preprocessed and converted to fourdimensional Gaussian Mixture Models that considered correlations between the different MR sequences. For each patient, a so-called tumor feature vector (0 probability-based classifier) was defined and used for grading. Biopsy was used as gold standard, and similarity based grading was compared to grading solely based on CE-T1w. Results Accuracy, sensitivity, and specificity of pure CET1w based glioma grading were 64.9%, 78.6%, and 56.5%, respectively. Similarity search based tumor grading allowed ifferentiation between low-grade (I or II) and high-grade (III) gliomas with an accuracy, sensitivity, and specificity of 83.8%, 78.6%, and 87.0%. Conclusion Our findings indicate that integration of perfusion arameters and CE-T1w information in a semiautomatic similarity search based analysis improves the potential of MR-based glioma grading compared to CET1w data alone.