The given paper suggested recognition algorithms of multilevel images 
of multi-character identification objects. These algorithms are based 
on application of linear (nonlinear) equivalent (nonequivalent) 
space-dependent similarity means of normalized matrix data as criterial 
(discriminant) functions. The results of modeling and experimental results 
have shown that such nonlinear-equivalent algorithms process higher 
discriminant properties and operating characteristics, especially 
in case of considerable (up to 40 %) noise level content of images. 
The suggested nonlinear-equivalent recognition algorithms possess good 
recognition quality, especially if the objects to be recognized are 
in noisy environment, if background noises have been added. 
The algorithms permit to recognize, as it has been proved by lab tests, 
in such noise conditions when traditional (correlation) algorithms fail. 
The developed program permits not only modeling of suggested algorithms, 
but it is used for recognition of given real objects.