Celestial　spectrum　recognition　is　an　indispensable　part　of　any　workable　automated　data　processing　system　of　celestial　objects.　Many　methods　have　been　proposed　for　spectra　recognition,　in　which　most　of　them　concerned　about　feature　extraction.　In　this　paper,　we　present　a　Bayesian　classifier　based　on　Kernel　Density　Estimation　(KDE)　which　is　composed　of　the　following　two　steps:　In　the　first　step,　linear　Principle　Component　Analysis　(PCA)　is　used　to　extract　features　to　decrease　computational　complexity　and　make　the　distribution　of　spectral　data　more　compact　and　useful　for　classification.　In　the　second　step,　namely　classification　step,　KDE　and　Expectation　Maximum　(EM)　algorithm　are　used　to　estimate　class　conditional　density　and　the　bandwidth　of　kernel　function　respectively.　The　experimental　results　show　that　the　proposed　method　can　achieve　satisfactory　performance　over　the　real　observational　data　of　Sloan　Digital　Sky　Survey　(SDSS).　©　2009　Copyright　SPIE　-　The　International　Society　for　Optical　Engineering.