We have developed a general purpose algorithm which finds genes by combining plural existing gene-finders. The algorithm has been implemented into a novel gene-finder named DIGIT. An outline of the algorithm is as follows. First, existing gene-finders are applied to an uncharacterized genomic sequence (input sequence). Next, DIGIT produces all possible exons from the results of gene-finders, and assigns them their exon types, reading frames and exon scores. Finally, DIGIT searches a set of exons whose additive score is maximized under their reading frame constraints. Bayesian procedure and a hidden Markov model are used to infer exon scores and search the exon set, respectively. We have designed DIGIT so as to combine the results of FGENESH, GENSCAN and HMMgene, and have assessed its prediction accuracy by using recently compiled benchmark data sets. For all data sets, DIGIT successfully discarded many false-positive exons predicted by individual gene-finders and yielded remarkable improvements in sensitivity and specificity at the gene level compared with the best gene level accuracies achieved by any single gene-finder.