Peeling outcomes of processing tomatoes were predicted using multivariate analysis of magnetic resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7-tesla MR system, and a multivariate data set was created from 28 different images. After imaging, the fruits were individually tagged and processed in a pilot peeling system. An expert grader then assessed the peeling outcome for each fruit; outcomes included “whole peel,”“some skin attached” and seven others. The multivariate analysis techniques of partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were used to predict peeling outcome from the 28 MR images. The PLS-DA model for the “whole peel” (best) outcome correctly classified 81% of the fruit that were in this category. The SIMCA model performed well for rejecting non-“whole peel” fruit but did not perform as well for identification of “whole peel” fruit.