Abstract Radiometric Identification (RAI) is the identification of wireless devices through their Radio Frequency (RF) emissions. In recent years, the research community has investigated it applying different methods and sets of statistical features extracted from the digitized RF emissions. In this paper, the authors investigate the application of Variational Mode Decomposition (VMD), recently introduced as an improvement to Empirical Mode Decomposition (EMD). VMD is applied to two sets of RF emissions from: wireless devices supporting Dedicated Short Range Communications (DSRC) at 5.9 GHz and Internet of Things wireless devices transmitting in the Industrial, Scientific and Medical (ISM) band at 2.4 GHz. Various machine learning algorithms have been used for classification and results are compared. Performances of VMD are evaluated against other approaches used in literature in Line of Sight (LOS) conditions, with Additive White Gaussian Noise (AWGN) and fading effects. Results show that VMD significantly outperforms other approaches.