Steel is by far the world’s most important, multi-functional, and most adaptable material. It is used in every aspect of our lives; in cars we drive, the buildings we work in, the homes in which we live, and countless other facets in between. The excellent mechanical properties of advanced steels are determined by their microstructure. Thus, correct microstructural classification is crucial for understanding of the structure-properties relationship and further development. While the microstructural characterization is widely spread and well known, the microstructural classification is done manually by human experts, which results in uncertainties due to subjectivity. The microstructure of advanced steels, such as advanced high strength steels, is a combination of different phases or constituents with complex substructures and its automatic classification is extremely challenging. Recently, the lack of correct and objective microstructural classification methods with high classification accuracy is a barrier for the further development of a new generation of advanced steels. The ultimate goal of our research is to develop novel objective microstructural classification methods having high accuracy (over 95%) for all types of multiphase steels. This goal leads us to utilize state-of-the-art Deep Learning methods due to their strong ability to learn high-level features from raw input data. A newly established laboratory at the Institute of Scientific Instruments is the Czech-first laboratory dedicated to the application of Artificial Intelligence in advanced material microstructural classification. The techniques developed within this project will result in considerable progress in the understanding of the microstructure-property relationship, and significantly support the development of novel steels with superior mechanical properties. The high importance and great application potential of this research secure the interest of both academic and industrial partners. It will enable us to be a strong and valuable partner in a consortium going to submit a ERC grant application.