Since in the proess of machine translation the source author is a human but the target translator is a machine, and up to now the human performance has been structurally more developed than that of the machines, sampling of human’s translation is more profitable for machine translation.This adberence makes the system ready to encounter the precise grammatical strategies of the source text efficiently. Empbasizing this conforming with human translation, the authors of this study try to scrutinize the performance of three systems of MT (Pars, Padide, and Google), translating English into Persian, to see to what extent they could observe this point.Systems tran.rlations are examined on the basis of Catford’s shifts and are then compared to corresponding human translations. Then a statistical analysis of' machines' performances in contrast to human performance is done; it is revealed that although Google's performance is much better than that of Pars and Padide, time systems stil need to be improved to efficiently cope with complicated structures.