Structural information from macromolecules provides key insights into the way complexes perform their biological functions. The reconstruction process leading to the final three-dimensional (3D) map is iterative and requires an initial volume to prime the refinement procedure. Particle images are aligned to this first reference and subsequently a new map is calculated from these particles. The accurate determination of an ab initio initial volume is still a challenging and open problem in cryo-electron microscopy (cryo-EM). Different algorithms are available to estimate an initial volume from the dataset. Some of these methods provide multiple candidate initial maps and users looking for robustness typically run different approaches. In this case, users arbitrarily evaluate the different obtained candidate maps, as we lack robust methods to objectively assess the accuracy of initial references. This workflow is subjective and error-prone preventing implementation of high-throughput data processing procedures. In this work, we present a robust method to determine the best initial map or maps from a set of ab initio initial volumes obtained from one or multiple different approaches. The method is based on evaluating multiple small subsets of candidate initial volumes and particle images through reference-based 3D classifications. Obtained 3D classes of accurate initial maps will result majoritarian and the respective attracted particles will be aligned with high angular accuracies. We have tested the proposed approach with structurally homogeneous and heterogeneous datasets providing satisfactory results with both type of data.