The recent cryo-EM resolution revolution enables the development of algorithms for direct de-novo modeling of protein structures into cryo-EM density maps. Here we present a machine learning based method for the detection of high confidence anchor amino acid residues in such a map. Such anchor residues can be exploited in several local de-novo modeling tasks, such as the reliable positioning of secondary structures, loop modeling and general fragment based modeling. In the experimental results we show the ability of the proposed procedure to locate and classify a significant number of amino acids in density maps of 3. 1 A° (or better) resolution. Our performance analysis indicates that the main factor affecting the detection accuracy is the lack of sufficient experimental data for the training stage of the algorithm. Thus, our method is expected to improve significantly in the near future, due to the rapid increase in the release of novel high resolution cryo-EM maps.