Methods of voice command (VC) recognition in heavy noise environments are required for precise work of speech information systems on the factory floor and in transport. The paper considers a speaker-dependent way of VC recognition for VCs belonging to a limited vocabulary and being recognized in heavy noise environments. For this purpose, VCs are transformed into cross-correlation portraits (CCPs), i.e. special images. The VC under recognition is referred to a class with a minimal distance (metric) between CCP of this command and model CCPs of the class. The authors elaborated algorithms for VC transformation into CCPs, a method for defining VC boundaries, ways of model command optimization and metric choice. As a result, a rather precise VC recognition in heavy noise environment was obtained.