The　development　of　non-linear　dynamic　theory　brought　a　new　method　for　recognising　and　predicting　the　complex　nonlinear　dynamic　behaviour.　Fractal　dimension　can　quantitatively　describe　the　non-linear　behaviour　of　vibration　signal.　In　the　present　paper,　the　capacity　dimension,　information　dimension　and　correlation　dimension　are　applied　to　classify　various　fault　types　and　evaluate　various　fault　conditions　of　rolling　element　bearing,　and　the　classification　performance　of　each　fractal　dimension　and　their　combinations　are　evaluated　by　using　SVMs.　Experiments　on　10　fault　data　sets　showed　that　the　classification　performance　of　the　single　fractal　dimension　is　quite　poor　on　most　data　sets,　and　for　a　given　data　set,　each　fractal　dimension　exhibited　different　classification　ability,　this　indicates　that　various　fractal　dimensions　contain　various　fault　information.　Experiments　on　different　combinations　of　the　fractal　dimensions　demonstrated　that　the　combination　of　all　these　three　fractal　dimensions　gets　the　highest　score,　but　the　classification　performance　is　still　poor　on　some　data　sets.　In　order　to　improve　the　classification　performance　of　the　SVM　further,　I　I　time-domain　statistical　features　are　introduced　to　train　the　SVM　together　with　three　fractal　dimensions,　and　the　classification　performance　of　the　SVM　is　improved　significantly.　At　the　same　time,　experimental　results　showed　that　the　classification　performance　of　the　SVM　trained　with　I　I　time-domain　statistical　features　in　tandem　with　three　fractal　dimensions　outperforms　that　of　the　SVM　trained　only　with　I　I　time-domain　statistical　features　or　with　three　fractal　dimensions.　(c)　2006　Elsevier　Ltd.　All　rights　reserved.