The principles of one-class classifiers are introduced, together with the distinctions between one-class/multiclass, soft/hard, conjoint/disjoint and modelling/discriminatory methods. The methods are illustrated using case studies, namely from nuclear magnetic resonance metabolomic profiling, thermal analysis of polymers and simulations. Two main groups of classifier are described, namely statistically based distance metrics from centroids (Euclidean distance and quadratic discriminant analysis) and support vector domain description (SVDD). The statistical basis of the D statistic and its relationship with the F statistic, χ2, normal distribution and T2 is discussed. The SVDD D value is described. Methods for assessing the distance of residuals to disjoint principal component models (Q statistic) and their combination with distance-based methods to give the G statistic are outlined. Copyright © 2011 John Wiley & Sons, Ltd.