To　ensure　the　validity　and　completeness　of　feature　extraction,　a　new　method　of　recognition　of　human　action　using　Zernike　moments-based　features　is　introduced.　In　the　proposed　method,　normalized　motion　history　image　for　motion　representation　is　valued.　Statistical　descriptions　are　then　computed　from　motion　history　image　using　Zernike　moment-based　features　for　the　following　recognition.　A　systematic　reconstruction-based　method　for　deciding　the　highest　order　of　Zernike　moments　required　in　a　classification　problem　is　developed.　Experiments　are　conducted　using　instances　of　eight　human　actions　(i.e.　eight　classes)　performed　by　different　subjects.　Experiment　results　show　that　Zernike　moment　features　for　the　recognition　of　human　action　are　superior　to　regular　moments　and　Hu　moments　in　the　accuracy　of　classification.