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The paper proposes a pattern recognition method using a modification of the class of logical decision functions presented in the form of decision tree. Instead of standard statements corresponding to the tree nodes, in which a variable is tested for a certain set of its values, a more general type of statements is used regarding the similarity of the point in question to different subsets of the observations. At the same time, to determine the degree of similarity, various metrics and subspaces of features can be used. This type of decision tree allows one to obtain more complex decision boundaries, which at the same time have a clear logical interpretation for the user. Several tree induction strategies are considered based on data transformation using support points selected with Relief, SVM, and k -means procedures. The method is experimentally investigated on the problem of tomographic images analysis, as well as on several synthetic datasets. Experiments have shown that the proposed method gives more accurate predictions than CART, SVM, kNN classifiers and deep convolutional neural network (AlexNet).

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