This chapter compares distance (UPGMA, NJ, LS and ME) vs. character-state approaches (ML, MP and BI) to tree building, highlighting their conceptual differences, strengths and weaknesses. Further, the tree-building methods are also compared and contrasted by classifying them into methods that use an algorithmic (UPGMA and NJ) vs. those that employ an optimality criterion (LS, ME, MP, ML and BI) with discussion on computational demands and accuracy. The limitations of clustering methods such as UPGMA that assume clocklike evolution are emphasised. Search algorithms and the problem of local vs. global optima in tree space are explained using a hill climbing analogy. Finally, concatenated vs. coalescent-based methods are introduced, showing how multi-gene data may require species-tree approaches to handle gene-tree discordance. The chapter equips readers with critical insights to select appropriate tree-building methods for their datasets.