This　chapter　discusses　seven　protein　secondary　structure　prediction　methods,　covering　simple　statistical-and　pattern　recognition-based　techniques.　The　prediction　methods　include　Chou-Fasman,　Garnier,　Osguthorpe　and　Robson　(GOR),　PHD,　neural　network　(NN)-based　protein　secondary　structure　prediction　(PSIPRED),　SPINE-X,　protein　secondary　structure　prediction　(PSSpred)　and　meta　methods.　The　chapter　assesses　the　performance　of　different　methods　using　the　Q3　measure.　It　investigates　the　accuracy　of　secondary　structure　prediction　for　target　proteins　by　the　alignment/threading　programs.　The　top-performing　methods,　for　example,　PSSpred,　PSIPRED,　and　SPINE-X,　are　consistently　developed　using　NNs,　which　suggests　that　NNs　are　one　of　the　most　suitable　pattern　recognition　algorithms　to　infer　protein　secondary　structure　from　sequence　profiles.　It　is　found　that　the　secondary　structure　prediction　by　the　alignment/threading　methods　that　combined　PSIPRED　with　other　informative　structural　features,　such　as　solvent　accessibility　and　dihedral　torsion　angles,　was　more　accurate.　©　2016　John　Wiley　&　Sons,　Inc.