Over the past few decades, with rapid growth in infrastructure, there has been tremendous growth in energy consumption. Along with this, more and more electronic appliances are added to the existing infrastructure every day. Furthermore, the existing energy bills just provide an aggregate number of units consumed but fail to provide any actionable details of appliance level usage. With the quest for long-term energy sustainability and to reduce this ever-growing energy consumption, research groups across the globe have started looking into energy disaggregation as a means of providing feedback. Some promising techniques like Non-intrusive appliance load monitoring (NIALM) have been adopted to provide detailed energy breakdown to the end consumer. Despite all these efforts, energy attribution to the electrical activities still seems to be a farfetched goal, especially in shared spaces. In this work, we have analyzed the possibility of using RF emissions from electronic appliances to detect electrical activity. Besides their known operation, these appliances are known to radiate high-frequency noise in the ambient environment, also called radio frequency interference (or RFI). Hence by utilizing these RFI emissions from electronic appliances, electrical activity from the appliance can be detected in multiple frequency bands and at varying distances. An 8-fit Gaussian mixture model (GMM) and k-peak finder are used for feature extraction from RFI data, followed by appliance activity recognition using k-nearest neighbor based classification. Appliance detection is performed with a mean accuracy of 71.9% across 7-class classification problem. Finally, characteristic features of RFI observed from these appliances, are discussed.Index Terms-RFI (Radio frequency interference), kNN (knearest neighbor), NIALM (Non-intrusive appliance load monitoring), SMPS (Switched mode power supply), HF (High frequency), LF (Low frequency), UPS (Uninterruptable power supply), CFL (Compact fluorescent lamp). A 1530-437X (c)