Echo　state　network　(ESN)　refers　to　a　novel　recurrent　neural　network　with　a　largely　and　randomly　generated　reservoir　and　a　trainable　output　layer,　which　has　been　utilized　in　the　time　series　prediction.　In　spite　of　that,　since　the　output　weights　are　computed　by　the　simple　linear　regression,　there　may　be　an　ill-posed　problem　in　the　training　process　for　ESN.　In　order　to　tackle　this　issue,　a　sparse　Bayesian　ESN　(SBESN)　is　given.　The　proposed　SBESN　attempts　to　estimate　the　probability　of　the　outputs　and　trains　the　network　through　sparse　Bayesian　learning,　where　independent　regularization　priors　should　be　implied　to　each　weight　rather　than　sharing　one　prior　for　all　weights.　Simulation　results　illustrate　that　the　SBESN　model　is　insensitivity　to　reservoir　size　and　completely　outperforms　other　models.