This paper presents a new application of mathematical morphology (MM) operators for low speed slew bearing condition monitoring. The MM operators were used as a signal processing step and feature extraction method for bearing vibration signals. Four basic MM operators; erosion, dilation, closing and opening, were studied. This paper also investigates another potential MM operator, namely gradient operator. Two common time domain features in bearing condition monitoring, namely root mean square (RMS) and kurtosis, were extracted from the processed signal. The study shows that the changes in bearing condition can be clearly detected from the extracted features (RMS and kurtosis) of the MM operators compared to RMS and kurtosis features extracted from the original vibration signal. The application of the method is demonstrated with laboratory run-to-failure slew bearing data acquired on a daily basis.