In proton magnetic resonance spectroscopic imaging (1H MRSI), the recorded spectra are often linear combinations of spectra from different cell and tissue types within the voxel. This produces problems for data analysis and interpretation. A sophisticated approach is proposed here to handle the complexity of tissue heterogeneity in MRSI data. The independent component analysis (ICA) method was applied without prior knowledge to decompose the proton spectral components that relate to the heterogeneous cell populations with different proliferation and metabolism that are present in gliomas. The ability to classify brain tumours based on IC decomposite spectra was studied by grouping the components with histopathology. To this end, 10 controls and 34 patients with primary brain tumours were studied. The results indicate that ICA may reveal useful information from metabolic profiling for clinical purposes using long echo time MRSI of gliomas.