Multivariate statistical analysis are important tools to assess soil quality. In this study, grey relational analysis and principal component analysis methods were implemented in order to identify the most influential variables affecting soil quality. For this aim, soil characteristics such as organic matter (SOM), mean weight diameter (MWD), aggregate stability (AS), dispersion ratio (DR), penetration resistance (PR), bulk density (ρ_b), total porosity (TP), air permeability (AP), permeability coefficient (PC), liquid limit (LL), plastic limit (PL), plasticity index (PI), shrinkage limit (SL), friability index (FI), optimum moisture content (OMC), and maximum dry bulk density (ρ_b-max) of soil specimens obtained from 45 different soil samples were measured. The PC (0.67), PI (0.65), MWD (0.65), PR (0.59), ρ_b-max (0.56), AP (0.55), ρ_b (0.54), TP (0.53), FI (0.53), DR (0.53), PL (0.53), LL (0.52), OMC (0.50), AS (0.50), SL (0.50) and SOM (0.47) were found to be the most significant variables associated with soil quality based on the mean values of grey relational coefficients. Grey relational grades calculated using three values obtained from principal component analysis displayed that Soil III 4% (0.98) had the highest quality, whereas Soil I control (0.34) had the lowest quality.Nearly a similar ranking occurred in two statistical calculation cases (GRA and GRA-PCA). Results obtained have shown that these two methods are suitable for solving complicated relationships between multiple factors and variables in soil research.