A new signal pre-processing algorithm for condition monitoring of rolling bearings is presented. By enhancing the statistical asymmetry of a measured vibration signal, it is shown to enhance weak impacts from outer-and inner race defects to aid feature extraction and fault detection. Unlike many popular methods such as the high-frequency resonance technique (e.g. envelope analysis), the proposed algorithm is based solely on linear time-domain processing of lowpass vibration signals and hence does not rely on non-linear processing or on the potentially difficult task of selecting an appropriate frequency band for analysis. Consequently, low computational complexity as well as ease of use and implementation can be obtained. A key feature of the method is the enhancement of fault impulses in noisy and distorted measurement data series. This is accomplished through a novel type of adaptive filtering that selectively enhances transient impulses while simultaneously suppressing noise and disturbances. Numerical results are presented demonstrating the new algorithm applied to accelerometer data from industrial environments