The paper introduces a graphical method called likelihood-mapping to visualize the phylogenetic content of a set of aligned sequences. This method is based on analyzing the maximum likelihoods for three fully resolved tree topologies that can be computed for four sequences. The likelihoods are represented as points inside an equilateral triangle, with different regions corresponding to star-like evolution, well-resolved phylogenies, and situations where it is difficult to distinguish between two trees. The location of these points in the triangle defines the mode of sequence evolution. For \( n \) sequences, the likelihoods for each subset of four sequences are mapped onto the triangle, providing a distribution that indicates whether the data are suitable for phylogenetic reconstruction. The method is applicable to nucleic acid and amino acid sequences and can be implemented in maximum likelihood tree reconstruction programs. The authors demonstrate the method using simulated and biological data sets, showing that it can effectively distinguish between star-like and tree-like evolution and provide insights into the phylogenetic content of the data.The paper introduces a graphical method called likelihood-mapping to visualize the phylogenetic content of a set of aligned sequences. This method is based on analyzing the maximum likelihoods for three fully resolved tree topologies that can be computed for four sequences. The likelihoods are represented as points inside an equilateral triangle, with different regions corresponding to star-like evolution, well-resolved phylogenies, and situations where it is difficult to distinguish between two trees. The location of these points in the triangle defines the mode of sequence evolution. For \( n \) sequences, the likelihoods for each subset of four sequences are mapped onto the triangle, providing a distribution that indicates whether the data are suitable for phylogenetic reconstruction. The method is applicable to nucleic acid and amino acid sequences and can be implemented in maximum likelihood tree reconstruction programs. The authors demonstrate the method using simulated and biological data sets, showing that it can effectively distinguish between star-like and tree-like evolution and provide insights into the phylogenetic content of the data.