One of the most important advances in biology in recent years may be the discovery of RNAs that can regulate gene expression. As one kind of such functional noncoding RNAs, microRNAs (miRNAs) form a class of endogenous 19-23-nucleotide RNAs that can have important regulatory roles in animals and plants by targeting transcripts for cleavage or translational repression. Since the discovery of the very first miRNAs, computational methods have been an invaluable tool that can complement experimental approaches to understand the biology of miRNAs. Most computational approaches associated with miRNA research can be classified into two broad categories, namely miRNA gene identification and miRNA target prediction. In this review, we summarize the principles of in silico prediction of miRNAs and their targets, and provide a comprehensive survey of specific methods that have been proposed in the field.