Health diagnosis of aero-generator is an issue of great importance to flight safety. One of the requirements is availability of extracting useful information from raw data. This paper presents a health diagnosis method based on neighborhood rough sets theory and support vector machine(SVM). Raw data were obtained from specific aero-generator test platform. An approach of attribute reduction using neighborhood rough sets theory is outlined and a diagnosis classifier is designed based on SVM to further carry out health diagnosis of aero-generator. The effectiveness of the proposed method is demonstrated through an experimental research. Result shows a better performance of the classifier that uses attributes reduction subsets as inputs, and also indicates the method may have wide popularization and application potential.