To reduce the development time and cost,an automatic sensory feature selection method is proposed for the systematic design of condition monitoring systems for machining operations.Force,acceleration,sound and acoustic emission sensors were used in high-speed milling operations.The time domain,frequency domain and wavelet analysis technique were employed to process the signals.Gradual tool wear was used for evaluating the proposed self-learning automated sensory feature selection approach.The experimental results show that the suggested algorithm can be applied in an automated,self-learning monitoring system for the selection of the most sensitive sensors.