An experienced process planner usually makes decisions based on comprehensive data without breaking it down into individual parameters. So, as the first phase it was necessary to establish a technological knowledge base, define features of the 2D drawing (independent variables), possible dependent variables, size and criteria for sample homogenization (principles of group technology) for carrying out analysis of variance and regression analysis. The second phase in the research was to investigate the possibility for easy automatic, direct finding and applying 3D features of an axial symmetric product to the regression model. The third phase in the research was to investigate the possibility for the application of neural networks in production time estimation and to compare the 224 results between the regression models and neural network models. The most important characteristic of our approach presented in this paper is estimation of production times using group technology, regression analysis and neural networks. Iskusni planeri tehnoloških procesa donose odluke temeljene sveobuhvatnim podacima bez bavljenja pojedinim parametrima. Tako, u prvoj je fazi bilo potrebno kreirati tehnološku bazu podataka, definirati značajke 2D crteža (nezavisne varijable), moguće zavisne varijable te veličinu i kriterije za homogenizaciju uzorka (načela grupne tehnologije) za provođenje analize varijance i regresijsku analizu. Druga faza istraživanja bilo je istraživanje mogućnosti jednostavnog i direktnog traženja i primjene 3D značajki aksijalno simetričnog proizvoda u regresijskom modelu. Treću fazu istraživanja činilo je proučavanje mogućnosti primjene neuronske mreže u procjeni proizvodnog vremena proizvoda te usporedba 224 rezultata između regresijskog modela i neuronske mreže. Najznačajnija karakteristika našeg primijenjenog pristupa, prezentiranog u radu, je procjena proizvodnih vremena primjenom grupne tehnologije, regresijske analize i neuronskih mreža.