Multivariate statistical techniques are useful for characterizing and estimating dynamic soil properties. Multivariate statistical techniques of cluster analysis (CA) and factor analysis (FA)/principal component analysis (PCA) were employed to comprehend the complex relationships between salinity, sodicity and some soil properties in irrigated part of Bafra Plain, Turkey. Seventy eight soil samples were randomly collected from 0–30 and 30–60 cm depths on September 2008. Cluster analysis grouped the sampling sites into three clusters based on similarity of soil properties. FA suggested a four-factor model that explained over 80.32% of the total variations in soil properties of 0-30 cm depth, with factor 1 comprising exchangeable sodium percentage (ESP), Na and EC; factor 2 comprising Mg, cation exchange capacity (CEC) and clay content; factor 3 comprising pH; and factor 4 comprising exchangeable Ca and Mg contents. FA suggested a three-factor model that explained 70% of the total variations in soil properties of 30-60 cm depths, with factor 1 comprising ESP, Na, pH and EC; factor 2 comprising CEC, clay and Mg concentrations; and factor 3 comprising Ca and K concentrations. The results demonstrated that cluster and principle component analyses are both useful in monitoring the soil degradation and help decision makers to take necessary precautions in advance.