Multiple tissue properties (T1, T2, diffusion, etc.) and system parameters can be acquired simultaneously with Magnetic Resonance Fingerprinting (MRF). The dictionary formation and matching steps may be confounded by the lack of certain tissue properties (e.g. magnetization exchange) in the dictionary and thus, the differentiation of tissues of interest may be suboptimal even though variable MRF signal evolutions set them apart. Here, as a dictionary-free alternative, spatio-temporal analysis of MRF time series is proposed for tissue characterization. Independent Component Analysis (ICA) based correlation analysis of prostate MRF data can distinguish between healthy and cancer tissue without explicit dictionary matching.