One　basic　approach　to　learn　Bayesian　networks　(BNs)　from　data　is　to　apply　a　search　procedure　to　explore　the　set　of　candidate　networks　for　the　database　in　light　of　a　scoring　metric,　where　the　most　popular　stochastic　methods　are　based　on　some　meta-heuristic　mechanisms,　such　as　Genetic　Algorithm,　Evolutionary　Programming　and　Ant　Colony　Optimization.　In　this　paper,　we　have　developed　a　new　algorithm　for　learning　BNs　which　employs　a　recently　introduced　meta-heuristic:　artificial　bee　colony　(ABC).　All　the　phases　necessary　to　tackle　our　learning　problem　using　this　meta-heuristic　are　described,　and　some　experimental　results　to　compare　the　performance　of　our　ABC-based　algorithm　with　other　algorithms　are　given　in　the　paper.