Selection of superior genotypes with wide adaptation across large complex environments or with specific adaptations to regional environments is an important issue for plant breeders.  Generally, selection for adaptability has been determined by performance in multi-environmental trials (MET).  However, the data produced by METs can be complex and selection decisions must consider the genotype main effects (G) in combination with the genotype x environment interactions (GE) simultaneously.  GGEbiplot software aids in the exploration of G and GE to identify superior genotypes with wide and specific adaptation over a wide range of environments.  Genotypes with wide adaptation perform well over many test locations, while genotypes with specific adaptation perform well in specific test locations.  The objective of this study was to determine how to effectively use GGEbiplot software with MET data to identify soft red winter wheat genotypes with wide and specific adaptation for the southeastern United States.  Identification of mega-environments and evaluation of genotype adaptation was determined with four years of data from the Uniform Southern Soft Red Winter Wheat Nursery.  Three methods were used to identify genotypes with wide and specific adaptation. Method 1 utilized the full data set to identify the most discriminating environments and the genotypes with wide adaptation for all tested locations.  Method 2 culled locations that were of little interest to the southeastern breeder.  Method 2 clustered the locations into mega-environments and identified genotypes that were widely adapted to their respective mega-environments.  Method 3 used just the locations within a mega-environment to identify genotypes with wide adaptation to the mega-environment and genotypes with specific adaptation to sub-regions within the mega-environment.  Results indicated that a reduction and reorganization of the data can increase the precision in comparing genotypes and aid in the identification of superior genotypes adapted to different mega-environments.