Among feature selection approaches, principal component analysis (PCA) has mainly been used [29]. Supervised methods and regressive techniques, such as discriminant analysis and partial least squares regression, are progressively being evaluated for their effectiveness in several studies [30, 34, 35]. Partial least squares regression (PLSR) is a powerful tool used in chemometrics [36] and has proven to be very versatile with applications in several research fields [32]. PLSR is a supervised method, relating the variations in one (PLS1) or multiple (PLS2) response variables to the variations of several predictors; the method selects successive orthogonal factors, which maximize the covariance between predictors and response variables. PLSR is particularly useful when the number of predictors is higher than the number of observations and/or when predictors are highly correlated, as frequently happens for soil indicators. Even though PLSR is usually adopted for predictive purposes, a large number of methods for variable selection in PLSR have been proposed. Among them, filter methods (loading weight vectors, PLS regression coefficients and variable importance on projection statistics-VIP) use the output of the PLS-algorithm to identify a subset of important variables [32]. In particular, VIP statistics are able to summarize the contribution of both predictors and response variables [37]. Principal component analysis (PCA) is a widespread procedure designed to summarize large datasets of correlated variables into a reduced number of components bearing the greatest part of the original information [38]. Variable weights or loadings of the retained components are useful to identify the variables that most contribute to each selected PC and investigate their relationships. Variables correlations and stepwise and canonical discriminant analysis (SDA and CDA) have been adopted in previous studies [15; 33; 39]. In particular, SDA allows the selection of variables which can best differentiate treatments or classes. In a study aimed to assess the effects of different management practices on soil physical quality (SPQ) in long-term field experiments, [PERSON] et al. [15] applied correlation analysis, PCA and SDA to select key soil physical quality indicators. The results highlighted the complementary and supplementary role of the three methods applied and the importance of simultaneously using different approaches to gain a complete understanding of the processes investigated. [PERSON] et al. [35], after a preliminary comparison of different multivariate approaches, used PLSR to define a standardized soil quality index for different soil conditions, natural vegetation or woodlands and different soil management, conventional tillage and no-tillage. Soil organic carbon, bulk density, carbon-nitrogen ratio and electrical conductivity were identified as the major variables influencing soil quality status and the index provided a comprehensive evaluation of the management investigated. [PERSON] et al. [24] used factor analysis to identify appropriate soil quality indicators under different land use and management practices. Soil organic carbon was the most dominant measured attribute; other key soil attributes were mainly physical indicators--i.e., field water capacity, air-filled porosity, bulk density- and pH. [PERSON] et al. [34] applied decision trees and assessed soil quality using visual soil assessment in the field and a limited number of physical and chemical soil properties. Regardless of the approach used, little research is available on the comparison and combined use of different statistical methods for selection of soil indicators. Therefore, the main objectives of this study were to (i) identify the most suitable variables for discriminating soil status under different soil management strategies and (ii) compare the performance of three multivariate statistical approaches to select soil indicators.