Profiling the usage of electrical devices within a smart home can be used as a method for determining an occupant's activities of daily living, which can support independent living of older adults. A nonintrusive load monitoring (NILM) system monitors the electrical consumption at a single electrical source and the operating schedules of individual devices are determined by disaggregating the composite electrical consumption waveforms. Nonintrusive load monitoring systems are able to detect the status of electrical devices based on the analysis of load signatures, the unique electrical behaviour of an individual device when it is in operation. This work uses a feature-based model, applying steady-state features (i.e., real power and reactive power) and transient features (i.e., switching on transient waveforms), for describing the load signatures of individual electrical devices. In this thesis, a NILM system is presented, which employs several common household electrical devices for data acquisition. Promising results are shown which demonstrate that real power and reactive power are useful steady-state features to identify electrical devices. Furthermore, experimental results showed that different electrical devices can be distinguished based on their switching on transient waveforms. Experimental results showed that the proposed approach can achieve high device recognition accuracy for electrical devices operating individually and simultaneously.