Stochastic context-free grammars (SC, FGs) Call be applied to the problems of folding, aligning and modeling families of homologous RNA sequences. SCFGs capture tile sequences’ common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. This paper discusses our new algorithm, Tree-Grammar EM, for deducing SCFG parameters automatically from unaligned, unfolded training sequences. Tree-Grammar EM, a generalization of tile HMM forward-backward algorithm, is based on tree grammars and is faster than tile previously proposed inside-outside SCFG training algorithm. Independently, Scan Eddy and Richard Durbin have introduced a trainable “covariance model” (CM) to perform similar tasks. We compare and contrast our methods with theirs.