A model for aeroengine condition monitoring based on fuzzy C-means clustering algorithm with cluster features weighting(FWFCM) was proposed. This model is composed of two parts: one is offline-learning module

in which the module parameters are iteratively computed

and the other is online-monitoring module

in which the realtime data can be classified according to the parameters. Then the realtime data are inputed into the offline-module and the module parameters are updated. Result shows that

compared with the data weighted fuzzy clustering algorithm(DWFCM) and the classic fuzzy C-means clustering algorithm (FCM)

the average condition recognition accuracy of the proposed method in offline module and in online module is 5.233% and 8.358% higher than the other two algorithms

respectively. It can be shown that the FWFCM works well for aeroengine condition monitoring with good rebustness and generalization

and has practical application value for uncertained aeroengine condition monitoring.