The present paper deals with an estimation of the water quality of the Struma river. Long-term trends, seasonal patterns and data set structures are studied by the use of statistical analysis. Nineteen sampling sites along the main river stream and di¬erent tributaries were included in the study. The sites are part of the monitoring net of the region of interest. Seventeen chemical indicators of the surface water have been measured in the period 1989 { 1998 in monthly intervals. It is shown that the water quality is relatively stable throughout the monitoring period, which is indicated by a lack of statistically signi cant trends for many of the sites and by chemical variables. Several seasonal patterns are observed at the sampling sites and four latent factors are identi ed as responsible for the data set structure.

The application of multivariate statistical methods to high mountain lakes monitoring data has offered some important conclusions about the importance of environmetric approaches in lake water quality assessment. Various methods like cluster analysis and principal components analysis were used for classification and projection of the data set from a big number of lakes from Pirin Mountain in Bulgaria. Additionally, self-organizing maps of Kohonen were constructed in order to solve some classification tasks. An effort was made to relate the maps with the input data in order to detect classification patterns in the data set. Thus, discrimination chemical parameters for each pattern (cluster) identified was found, which enables better interpretation of the ecological state of the system. A methodology for application of combination of different environmetric methods was suggested as a pathway to interpret high mountain lake waters monitoring data.

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