In this paper, we present our preliminary results towards a novel framework for non intrusive monitoring of home appliances using wireless signals such as those of Wi-Fi. The framework relies on the fact that the state - ON or OFF - of some home appliances causes detectable changes in the characteristics of the wireless channel. For proof of concept demonstrations, we select a number of home appliances whose operation is either characterized by moving parts or by generation of radio frequency interference (RFI) - properties which we show can be detected by analysing reflected radio frequency energy in the wireless channel. Using software-defined radios with a WiFi-like physical layer, we develop a prototype system for the detection of the following home appliances: (a) Ceiling Fan, (b) Pedestal Fan, (c) Washing Machine, and (d) Microwave Oven. We identify relevant features on the measured Channel State Information (CSI) that depict the environmental changes due to state of different home appliances. These features are used by a subsequent machine learning stage to classify the state and type of the home appliance. By achieving an average validation accuracy of 90.11% using a Support Vector Machine (SVM) classifier, our results indicate that the proposed framework has the potential for a cheap, easy to install, and an ubiquitous alternative to conventional non-intrusive load monitoring (NILM).