Classification　of　motor　imagery　electroencephalogram　(　EEG)　is　one　of　the　most　important　technologies　for　BCI.　To　improve　the　accuracy,　this　paper　introduces　a　classification　system　based　on　Multilayer　Extreme　Learning　Machine　(ML-ELM).　In　the　system,　the　combination　of　PCA　and　LDA　is　chosen　as　the　method　of　feature　extraction　and　the　ML-ELM　is　used　to　classify.　The　ML-ELM　has　not　only　the　advantage　which　ELM　has　but　also　better　performance　than　ELM.　In　the　experiment,　our　method　is　compared　with　the　methods　based　on　ELM,　such　as　kernel-ELM,　Constrained-ELM　and　V-ELM,　and　some　state-of-the-art　methods　on　the　same　dataset.　The　experimental　results　show　that　ML-ELM　is　much　more　suitable　for　motor　imagery　EEG　data　and　has　better　performance　than　the　others.