Machine learning deals with the development of algorithms that allows a computer to develop autonomous behavior based on empirical data from sensors or databases. This paper explores the possibilities of the prepress automation with application of machine learning techniques in which decision-making would be left to artificial intelligence. Paper addresses the problem of categorizing large amounts of documents whose processing by workers is highly impractical due to its large scale. Besides that, it is time-consuming and it could be subject of human errors. This paper examines and compares different approaches that reduce the need for human labor and categorizing is left to computer program with the intention of speeding up production and reducing the cost of prepress. The introductory section contains an informative overview of machine learning, interesting achievements in this scientific field and it discusses various approaches to machine learning automation. Main section examines reliability and reasons for machine learning automation and, dependent on the research results, it lays down the advantages and disadvantages of such solutions. It is assumed that the preparation of documents with this kind of automation is at least as good, but many times faster than preparations using human labor.