Visualization of multivariate multidimensional data sets is a challenging task, especially without use of adequate tools and methods. In the last few years, parallel coordinate plots became quite popular and accepted as a very efficient multivariate visualization technique. The aim of this paper was to explore how parallel coordinates can be used in analysis of winter wheat quantitative traits. Data set is obtained from experiment set up by a completely randomized design with two treatments and four replicates. Ten variables (plant height, spike length, stem length, plant weight, spike weight, grain weight per spike, 1000 kernel weight, number of fertile and sterile spikelets per spike and total number of spikelets per spike) and fifty-five winter wheat genotypes were analysed in this paper. In parallel coordinate plots observations are shown as series of unbroken lines, passing through parallel axes, where each axes represents a different variable. Advantage of parallel coordinates, compared to other visualization techniques, is that they can represent multivariate data in two dimensions. From such representation, outliers and grouping among observations are easily detectable. Correlation among variables can also be easily detected from suchrepresentation. Although parallel coordinates cannot efficiently explore details, they are a good technique for visualization of multivariate data sets and they can be used for exploratory analysis of wheat quantitative traits.