A Bayesian framework that integrates chromatographic and rheological measurements to infer the structure of an unknown binary linear blend, with a high molar mass tail, is proposed and explored. A visualization method based on clustering of multidimensional data is introduced to facilitate the comprehension of the joint probability distribution that results from the Bayesian analysis. The relationship between the rheological and chromatographic data is found to be synergetic: the simultaneous analysis of both sets of data circumvents problems of degeneracy and insensitivity associated with rheology and chromatography, respectively.