A client working on blending his wine wanted to get as close as possible to a known wine with different ingredients. To help the client do this, we created a mixture design with four ingredients for 16 samples to compare with the known blend. The samples were tasted randomly, and the panel was asked to create groups of similar samples and describe the group. The result was a distance matrix developed with the help of a JMP script. This matrix was processed by a multidimensional scaling to obtain a map that was easy to describe to the panel. A K-means classification was used to find the samples close to the target. Finally, the distance between the target and the other sample was calculated and represented by a contour plot to show the best part of the mixture design. The terms used by the taster to describe the groups were processed by JMP Text Explorer and then by AFCM to show a map with samples and terms to better describe each sample's position using sensory properties.