The use of electromyography (EMG) for the control of upper-limb prostheses has received great interest in neurorehabilitation engineering since decades. Important advances have been performed in the development of machine learning algorithms for myocontrol. This paper describes a novel adaptive filter for EMG preprocessing to be applied as conditioning stage for optimal subsequent information extraction. The aim of this filter is to improve both the quality (signal-to-noise ratio) and the selectivity of the EMG recordings. The filter is based on the classic common average reference (CAR), often used in EEG processing. However, while CAR is stationary, the proposed filter, which is referred to as adaptive common average reference (ACAR), is signal-dependent and its spatial transfer function is adapted over time. The ACAR filter is evaluated in this study for noise reduction and selectivity. Furthermore, it is proven that its application improves the performance of both pattern recognition and regression methods for myoelectric control. It is concluded that the proposed novel filter for EMG conditioning is a useful preprocessing tool in myocontrol applications.