A data classification method using a Boltzmann machine algorithm is proposed in this paper. The purpose of the classification is to determine local submodels which describe the data of each class. The Boltzmann machine is enabled to estimate both classification and submodel parameters at the same time. The method is also used in conjunction with a set of multilayer-perceptron class models, in which the relevant algorithm work on the basis of calculated expectations, rather than actual stochastic behaviors. The network is shown to be successful for classification of iris data and wine data.