In some classification tasks, all patterns in a field, such as digits in a ZIP-code image, originate from the same, but unknown, source (writer/print style). The class-conditional feature distributions depend on the source of the patterns. Several sources may share the same distribution, or style. The style-conditional distributions are estimated from the training set. The optimal field-classifier computes the class-conditional field-feature-probabilities as the sum of class-and-style-conditional field-feature-probabilities, weighted by the prior probabilities of the styles. We compare the decision regions and error rates of style-weighted classification with both conventional singlet and top-style classification in a minimal family of examples, and discuss some related practical considerations.