Place　or　scene　recognition　is　an　important　competence　of　mobile　robots　for　operating　in　a　real　dynamic　environment.　Latent　Dirichlet　Allocation　(LDA),　a　popular　probabilistic　model,　can　achieve　outstanding　performance　in　image　recognition,　and　it　has　been　attracted　the　attention　of　a　large　number　of　researchers.　Parameter　estimation　is　a　key　step　for　the　learning　procedure　of　LDA.　In　this　paper,　we　propose　a　novel　place　recognition　approach　based　on　LDA　using　Markov　chain　Monte　Carlo　(MCMC)　for　approximate　inference　technique.　Firstly,　the　training　images　of　each　category　are　represented　as　a　set　of　different　themes.　And　MCMC　is　employed　to　estimate　parameters　of　LDA　instead　of　variational　inference.　Then　an　unknown　test　image　can　be　recognized　according　to　its　themes　distribution.　Experimental　results　show　that　our　method　can　perform　better　than　the　variational　inference　algorithm　over　IDOL2　database　and　our　own　image　set　captured　under　different　imaging　conditions　in　our　campus.　©　2013　IEEE.