An　echo-state　network　(ESN)　is　an　effective　alternative　to　gradient　methods　for　training　recurrent　neural　network.　However,　it　is　difficult　to　determine　the　structure　(mainly　the　reservoir)　of　the　ESN　to　match　with　the　given　application.　In　this　paper,　a　growing　ESN　(GESN)　is　proposed　to　design　the　size　and　topology　of　the　reservoir　automatically.　First,　the　GESN　makes　use　of　the　block　matrix　theory　to　add　hidden　units　to　the　existing　reservoir　group　by　group,　which　leads　to　a　GESN　with　multiple　subreservoirs.　Second,　every　subreservoir　weight　matrix　in　the　GESN　is　created　with　a　predefined　singular　value　spectrum,　which　ensures　the　echo-sate　property　of　the　ESN　without　posterior　scaling　of　the　weights.　Third,　during　the　growth　of　the　network,　the　output　weights　of　the　GESN　are　updated　in　an　incremental　way.　Moreover,　the　convergence　of　the　GESN　is　proved.　Finally,　the　GESN　is　tested　on　some　artificial　and　real-world　time-series　benchmarks.　Simulation　results　show　that　the　proposed　GESN　has　better　prediction　performance　and　faster　leaning　speed　than　some　ESNs　with　fixed　sizes　and　topologies.