This study aims to evaluate the spatial and temporal changes in water quality of Karaboğaz Stream using statistical methods, to determine the main pollutant sources and to demonstrate the water quality classes. Water-quality data were obtained monthly (November 2016-October 2017) from 10 stations and considering 28 parameters. Temporal and spatial variations of Stream surface water quality were analyzed using multivariate statistical techniques on datasets, including agglomerative hierarchical clustering analysis (HCA) and principal component analysis (PCA). The analysis refers to the four main components responsible for the data structure and accounts for 87.41% of the total variance of the dataset. The root of these main components is generally related to the point source pollution (anthropogenic), nonpoint source pollution (agricultural activities) and natural processes (climate, soil and rock erosion). The temporal analysis of the water quality with HCA indicated that autumn is different from the other seasons. This study presents the practicality of various statistical methods in assessing and interpreting water-quality data to monitor and increase the management efficiency. When designing the most appropriate action plans for managers to control pollution, clearer, understandable information can be achieved using these methods and interpreting raw data.