The present paper deals with the successive application of self-organizing map (SOM) classification and Hasse diagram technique (HDT) as chemometric tool for assessment of river water quality. The study is carried out by using long-term water quality monitoring data from the Struma River catchment, Bulgaria. The advantages of the SOM algorithm for advanced visualization and classification of large data sets are used for proper selection of chemical parameters being most effective in quality assessment. The proper variable selection combined with some state directives for surface water quality parameters was then used for performing a new data classification separating the objects of interest (sampling sites) into specific patterns. The simultaneous application of the SOM methodology and partial order theory allows to visualize the spatial and temporal evolution of water quality parameters. Thus, it can be seen that the nitrate loads are decreasing with time and the high specificity of a certain sampling station with respect to its water quality data pattern is changing. Copyright © 2010 John Wiley & Sons, Ltd.