Magnetic Resonance (MR) is a powerful technique for generating detailed information about materials and tissues, but its acquisitions are often qualitative and limited to a few properties. This paper introduces Magnetic Resonance Fingerprinting (MRF), a novel paradigm that allows non-invasive quantification of multiple material or tissue properties simultaneously through innovative data acquisition, post-processing, and visualization techniques. MRF uses pseudorandomized acquisition to create unique signal evolutions or "fingerprints" that are simultaneously functions of multiple material properties. Pattern recognition algorithms match these fingerprints to a predefined dictionary of predicted signal evolutions, translating them into quantitative maps of MR parameters. MRF is related to compressed sensing and offers several advantages, including the ability to acquire fully quantitative results in a time comparable to traditional qualitative MR scans, reduced sensitivity to measurement errors, and the potential for multiparametric MR analyses. The study demonstrates the feasibility of MRF through phantom and in vivo experiments, showing high accuracy and efficiency compared to conventional methods. MRF also demonstrates robustness against motion and other acquisition errors, and has the potential to improve the quality of MR images using current scanners or reduce the cost of exams.Magnetic Resonance (MR) is a powerful technique for generating detailed information about materials and tissues, but its acquisitions are often qualitative and limited to a few properties. This paper introduces Magnetic Resonance Fingerprinting (MRF), a novel paradigm that allows non-invasive quantification of multiple material or tissue properties simultaneously through innovative data acquisition, post-processing, and visualization techniques. MRF uses pseudorandomized acquisition to create unique signal evolutions or "fingerprints" that are simultaneously functions of multiple material properties. Pattern recognition algorithms match these fingerprints to a predefined dictionary of predicted signal evolutions, translating them into quantitative maps of MR parameters. MRF is related to compressed sensing and offers several advantages, including the ability to acquire fully quantitative results in a time comparable to traditional qualitative MR scans, reduced sensitivity to measurement errors, and the potential for multiparametric MR analyses. The study demonstrates the feasibility of MRF through phantom and in vivo experiments, showing high accuracy and efficiency compared to conventional methods. MRF also demonstrates robustness against motion and other acquisition errors, and has the potential to improve the quality of MR images using current scanners or reduce the cost of exams.