Most automatic weather stations sample visibility with optical devices, which are vulnerable to interference from rain, snow and dust, resulting in the inaccuracy or missing of visibility data.To address this and provide complete data for meteorological prediction, this paper proposes a Support Vector Machine-Back Propagation (SVM-BP) neural network based data quality control method for visibility data correction and filling.First, the grey correlation analysis is used to select meteorological elements closely related with visibility.Second, the visibility data of different terrains are estimated by SVM and the BP neural network independently, which are then combined by optimal combination weights.Historical weather visibility data from Anhui Meteorological Bureau are used to verify the proposed method.The results show that compared with the independent SVM or the BP neural network, the combined estimation has smaller mean error and higher overall accuracy.The proposed SVM-BP neural network method provides an effective tool for visibility data filling, thus lays theoretical basis for weather forecasting, weather analysis, meteorological research and public service.