With genome sequencing projects producing huge amounts of sequence data, database sequence similarity search has become a central tool in bioinformatics to identify potentially homologous sequences. It is thus widely used as an initial step for sequence characterization and annotation, phylogeny, genomics, transcriptomics, and proteomics studies. Database similarity search is based upon sequence alignment methods also used in pairwise sequence comparison. Sequence alignment can be global (whole sequence alignment) or local (partial sequence alignment) and there are algorithms to find the optimal alignment given particular comparison criteria. However, as database searches require the comparison of the query sequence with every single sequence in the database, heuristic algorithms have been designed to reduce the time required to build an alignment that has a reasonable chance to be the best one. Such algorithms have been implemented as fast and efficient programs (Blast, FastA) available in different types to address different kinds of problems. After searching the appropriate database, similarity search programs produce a list of similar sequences and local alignments. These results should be carefully examined before coming to any conclusion, as many traps await the similarity seeker: paralogues, multidomain proteins, pseudogenes, etc. This chapter presents points that should always be kept in mind when performing database similarity searches for various goals. It ends with a practical example of sequence characterization from a single protein database search using Blast.