Magnetic Resonance Fingerprinting signal evolutions are sensitized to certain tissue properties during data acquisition. The matching step can be suboptimal due to dictionary limitations or tissue related constraints (e.g. partial volume, magnetization exchange). Here, we propose to apply Independent Component Analysis (ICA) to 4D MRF data after image reconstruction without explicit dictionary matching for tissue characterization, lifting the requirement for a relaxation model. ICA of whole brain MRF data segments the brain into multiple components with single tissue types such as gray matter, white matter and CSF for healthy subjects and also tumor in the case of glioblastoma patients.