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In this paper we consider use of some special log-linear models and minimum delta estimation in the multivariate classification problem posed by Martin and Bradley (1972). We first define these models, called log-difference models, and show that the minimum risk classification rule depends only on a certain subset of the new parameters. We then review minimum delta estimation, in particular the minimum delta estimator, the approximate minimum delta estimator, and their existence properties. Two examples are worked. The first involves detergent preference and illustrates how extensions to the case in which not all variables are dichotomous may be obtained through the use of orthogonal polynomials. The second example involves infant hypoxic trauma, and many cells are empty. The existence conditions are used to find a model for which estimates a cell frequencies exist and are in good agreement with the observed data. (Author)