The purpose of our research is to substantiate the use sequence of clustering methods for grouping the morphological characteristics of lettuce varieties var. capitata for the test for the difference. For analytical research, the results of the qualification examination of lettuce varieties for distinctness, uniformity, and stability were used. Analyzed data for the period from 2009 to 2019 year, which obtained by international requirements Guidelines for the conduct of tests for distinctness, uniformity, and stability (TG/13/11). For more precisions, identification varieties for distinctness were used hierarchical cluster analysis and Machine Learning Method in sequence. It was experimentally revealed that the most adequate model of similar varieties groups of lettuce is formed when the attribute "head: size" is used as the target variable and the attribute "head density" as the focal variable. Findings indicate the prospects of using the algorithm of the nearest neighbors in the identification of similar varieties o Lactuca sativa L. var. capitata.