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In this master thesis project the applicability of using only smartphone standalone data to monitor the condition of rolling element bearings is investigated. Using a smartphone instead of expensive measurement equipment and data acquisition systems can result in a fast and cost-effective methodology to monitor the health status of rolling element bearings. The shaft speed extraction is based on video images acquired by the smartphone camera. This methodology exploits the deformation of the video image due to the rolling shutter effect of the smartphone camera. The bearing damage detection is based on the audio of the smartphone video. The audio is captured in stereo by a dual plug-in microphone. The acoustic data is processed by three different signal processing methodologies and subsequently the resulting envelope spectra are analysed to check for bearing fault signatures. The methodologies used are: Firstly, a demodulation of the raw acoustic data by applying a Hilbert transform. Secondly, a demodulation of the acoustic data after filtering the raw acoustic data with an ideal bandpass filter based on the characteristics of the Kurtogram. Thirdly, transforming the acoustic data to the bi-frequency domain by calculating the spectral coherence. The different methodologies are validated by performing experiments with a Mild Inner Race, Small Outer Race and Large Outer Race damaged bearing running at shaft speeds in the range from 5 to 40 revolutions per second. The acoustic data resulting from a reference microphone resulted in promising results in order to detect bearing damages when using the kurtogram-based bandpass filter or the spectral coherence. Unfortunately, the acoustic data resulting from the smartphone audio has a cut-off frequency at 14.5 kHz, due to data compression. This lead to the non-detection of small bearing damages, because the signal-to-noise ratio was too low in the available frequency bands.