Automatic　image　annotation　is　a　crucial　area　in　computer　vision,　which　plays　a　significant　role　in　image　retrieval,　image　description,　and　so　on.　Along　with　the　internet　technique　developing,　there　are　numerous　images　posted　on　the　web,　resulting　in　the　fact　that　it　is　a　challenge　to　annotate　images　only　by　humans.　Hence,　many　computer　vision　researchers　are　interested　in　automatic　image　annotation　and　make　a　great　effort　in　optimizing　its　performance.　Automatic　image　annotation　is　a　task　that　assigns　several　tags　in　a　limited　vocabulary　to　describe　an　image.　There　are　many　algorithms　proposed　to　tackle　this　problem　and　all　achieve　great　performance.　In　this　paper,　we　review　seven　algorithms　for　automatic　image　annotation　and　evaluate　these　algorithms　leveraging　different　image　features,　such　as　color　histograms　and　Gist　descriptor.　Our　goal　is　to　provide　insights　into　the　automatic　image　annotation.　A　lot　of　comprehensive　experiments,　which　are　based　on　Corel5K,　IAPR　TC-12,　and　ESP　Game　datasets,　are　designed　to　compare　the　performance　of　these　algorithms.　We　also　compare　the　performance　of　traditional　algorithms　employing　deep　learning　features.　Considering　that　not　all　associated　labels　are　annotated　by　human　annotators,　we　leverage　the　DIA　metrics　on　IAPR　TC-12　and　ESP　Game　datasets.　©　2020,　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.