Most common approach to interpretation of genotype-environment interaction (GxE interaction) for breeding and variety recommendation purposes is stability and adaptation analysis of genotypes in a target region of cultivation. The aim of this paper was to review very rich both scientific and practical achievements in statistical methodology of stability and adaptation analysis of genotypes. The methods used could be divided into three groups: univariate parametric methods, univariate nonparametric methods and multivariate methods. Most of the methods are based on both fixed and mixed linear and multiplicative models. Stability measures defined in many models are useful to evaluating similarity of a genotype trait response to environmental conditions in a target region to a norm (concept) of dynamic (agronomic) stability which has been introduced by Becker and Leon (1988). Joint regression models belong to those most of ten used in considered studies. Recently, multivariate models and methods have become a standard statistical tool in interpreting GxE interaction for various purposes. They are extensions of the conventional joint regression models, both fixed and mixed ones. Among them, AMMI models and related methods have been most effective and, then of ten used in field experimentation. The AMMI models incorporate both additive and different kinds of multiplicative components. Some parametric and nonparametric criteria, incorporating both yield-means and yield-stability measures can be effective to selecting such genotypes which productivity in a target region indicates their wide adaptation.