The angular wheel speed of a vehicle is estimated by tracking the frequency of chassis vibrations measured with an accelerometer. A Bayesian filtering framework is proposed, allowing for straightforward incorporation of supporting information. The framework is evaluated on a large number of experimental test drives, showing comparable performance to the standard periodogram method. We then demonstrate the flexibility of the framework using accelerometer information in two ways, combining the high-frequency vibrations with low-frequency information about the vehicle acceleration. This is shown to improve robustness and resolve many cases where stand-alone frequency tracking fails.