Likelihood-mapping is a graphical method to visualize the phylogenetic content of a sequence alignment. It uses the maximum likelihoods of three fully resolved tree topologies for four sequences, represented as points inside an equilateral triangle. The triangle is divided into regions that indicate the mode of sequence evolution, such as star-like, well-resolved, or ambiguous. For n sequences, the likelihoods of each subset of four sequences are mapped onto the triangle, showing the phylogenetic content of the data. The method is applicable to nucleic acid sequences, amino acid sequences, or any alphabet with a model of sequence evolution. It allows visualization of the tree-likeness of all quartets in a single graph, enabling quick interpretation of phylogenetic content. The method was exemplified using simulated sequences and biological data sets. The results showed that likelihood-mapping can distinguish between star-like and tree-like evolution, and it is useful for analyzing the support of internal branches in a tree. The method is based on the analysis of quartets, which are the basic ingredients for tree reconstruction. It also allows testing of internal edges of a tree. The results of likelihood-mapping analysis depend on sequence length, and the method has reasonable predictive power. However, further statistical tests are needed to assess the significance of clusters or deviations from tree-likeness. The interpretation of likelihood-mapping results is strongly influenced by sequence length, and the method can be used to visualize the phylogenetic content of a data set.Likelihood-mapping is a graphical method to visualize the phylogenetic content of a sequence alignment. It uses the maximum likelihoods of three fully resolved tree topologies for four sequences, represented as points inside an equilateral triangle. The triangle is divided into regions that indicate the mode of sequence evolution, such as star-like, well-resolved, or ambiguous. For n sequences, the likelihoods of each subset of four sequences are mapped onto the triangle, showing the phylogenetic content of the data. The method is applicable to nucleic acid sequences, amino acid sequences, or any alphabet with a model of sequence evolution. It allows visualization of the tree-likeness of all quartets in a single graph, enabling quick interpretation of phylogenetic content. The method was exemplified using simulated sequences and biological data sets. The results showed that likelihood-mapping can distinguish between star-like and tree-like evolution, and it is useful for analyzing the support of internal branches in a tree. The method is based on the analysis of quartets, which are the basic ingredients for tree reconstruction. It also allows testing of internal edges of a tree. The results of likelihood-mapping analysis depend on sequence length, and the method has reasonable predictive power. However, further statistical tests are needed to assess the significance of clusters or deviations from tree-likeness. The interpretation of likelihood-mapping results is strongly influenced by sequence length, and the method can be used to visualize the phylogenetic content of a data set.