In this paper we present an application to food science of image processing technique. We describe a method for determining fat content in beef meat. The industry of meat faces a permanent need for improved methods for meat quality evaluation. Researchers want improved techniques to deepen their understanding of meat features. Expectations of consumers for meat quality grow constantly, which induces the necessity of quality control. Recent advances in the area of computer and video processing have created new ways to monitor quality in the food industry. We investigate the use of a new technology to control the quality of food: NMR imaging. The inherent advantages of NMR images are many. Chief among these unprecedented contrasts between the various structures present in meat like muscle, fat, and connective tissue. Moreover, the three-dimensional nature of the NMR method allow us to analyze isolated cross-sectional slices of the meat and to measure the volumetric content of fat, not only the fat visible on the surface. We propose a segmentation algorithm for the detection of fat together with a filtering technique to remove intensity inhomogeneities in NMR images caused by non-uniformities of the magnetic field during acquisition. Measurements have been successfully correlated with chemical analysis and digital photography. Results show that the NMR technique is a promising non-invasive method to determine the fat content in meat.