In　this　paper,　we　use　natural　gradient　algorithm　to　control　the　shape　of　the　conditional　output　probability　density　function　for　the　stochastic　distribution　systems　from　the　viewpoint　of　information　geometry.　The　considered　system　here　is　of　multi-input　and　single　output　with　an　output　feedback　and　a　stochastic　noise.　Based　on　the　assumption　that　the　probability　density　function　of　the　stochastic　noise　is　known,　we　obtain　the　conditional　output　probability　density　function　whose　shape　is　only　determined　by　the　control　input　vector　under　the　condition　that　the　output　feedback　is　known　at　any　sample　time.　The　set　of　all　the　conditional　output　probability　density　functions　forms　a　statistical　manifold　(M),　and　the　control　input　vector　and　the　output　feedback　are　considered　as　the　coordinate　system.　The　Kullback　divergence　acts　as　the　distance　between　the　conditional　output　probability　density　function　and　the　target　probability　density　function.　Thus,　an　iterative　formula　for　the　control　input　vector　is　proposed　in　the　sense　of　information　geometry.　Meanwhile,　we　consider　the　convergence　of　the　presented　algorithm.　At　last,　an　illustrative　example　is　utilized　to　demonstrate　the　effectiveness　of　the　algorithm.　©　2013　Elsevier　B.V.