Homology detection is critical to genomics. Identifying homologous sequence allows us to transfer information gathered in one organism to another quickly and with a high degree of confidence. Non-coding RNA (ncRNA) presents a challenge for homology detection, as the primary sequence is often poorly conserved and de novo structure prediction re-mains difficult. This chapter introduces methods developed by the Rfam database for identifying “families ” of homologous ncRNAs from single “seed ” sequences using manually curated alignments to build powerful statistical models known as covariance models (CMs). We provide a brief overview of the state of alignment and secondary structure prediction algorithms. This is followed by a step-by-step iterative protocol for iden-tifying homologs, then constructing an alignment and corresponding CM. We also work through an example, building an alignment and CM for the bacterial small RNA MicA, discovering a previously unreported family of divergent MicA homologs in Xenorhabdus in the process. This chap-ter will provide readers with the background necessary to begin defining their own ncRNA families suitable for use in comparative, functional, and evolutionary studies of structured RNA elements. 1