The accurate quantification of sensory difference/similarity between foods, as well as consumer acceptance/preference and concepts, is greatly needed to optimize and maintain food quality. The R-Index is one class of measures of the degree of difference/similarity, and was originally developed for sensory difference tests for food quality control, product development, and so on. The index is based on signal detection theory and is free of the response bias that can invalidate difference testing protocols, including categorization and same-different and A-Not A tests. It is also a nonparametric analysis, making no assumptions about sensory distributions, and is simple to compute and understand. The R-Index is also flexible in its application. Methods based on R-Index analysis have been used as detection and sensory difference tests, as simple alternatives to hedonic scaling, and for the measurement of consumer concepts. This review indicates the various computational strategies for the R-Index and its practical applications to consumer and sensory measurements in food science.