Most of agronomic traits are quantitative in their nature, so environment plays a big role in their expression. Combinations of different stress resistance genes and genes involved in adaptation mechanisms in plants are the main reason for developing genotypes that have ability to maintain constant yields under stress conditions on field. Because of the big number of agronomic traits in wheat and its complex correlations, multivariate statistycal analysis like principal component analysis (PCA) are used for data reduction and emphasis of variables that contributed the most in total variability of the sample. The main goal of this analysis is to explore the influence of the environmental factors on agronomic traits of wheat and grouping of the cultivars based on the change of agronomic traits in two years of experiment using PCA. Analysis included 40 cultivars from Austria, France, Croatia and Hungary. They were sown on experimental fields in seasons 2013./2014. and 2014./2015. Analised agronomic traits in this paper were plant height, days to heading and yield. PCA from both years of experiment showed similarity in correlations between agronomic traits and grouping of cultivars which was mostly influenced by plant height. Analysis highlighted cultivars that stand out in aspect of relatively big yields, low plant height and small number of days to heading in both years. PCA analysis of changes in agronomic traits pointed cultivars that had the smallest differences in two or more analised traits.