Optical Character Recognition (OCR) is a very extensive branch of pattern recognition. The existence of super effective software designed for omnifont text recognition, capable of handling multiple languages, creates an impression that all problems in this field have already been solved. Indeed, focus of research in the OCR domain has constantly been shifting from offline, typewritten, Latin character recognition towards Asiatic alphabets, handwritten scripts and online process. Still, however, it is difficult to come across an elaboration which would not only cover the topic of numerous feature extraction methods for printed, Latin derived, isolated characters conceptually, but which would also attempt to implement, compare and optimize them in an experimental way. This paper aims at closing this gap by thoroughly examining the performance of several statistical methods with respect to their recognition rate and time efficiency.