Information on residential power consumption patterns disaggregated at the single-appliance level is an essential requirement for energy utilities and managers to design customized energy demand management strategies. Non-Intrusive Load Monitoring (NILM) techniques provide this information by decomposing the aggregated electric load measured at the household level by a single-point smart meter into the individual contribution of each end-use. Despite being defined non-intrusive, NILM methods often require an intrusive data sampling process for training purpose. This calibration intrusiveness hampers NILM methods large-scale applications. Other NILM challenges are the limited accuracy in reproducing the end-use consumption patterns and their trajectories in time, which are key to characterize consumers’ behaviors and appliances efficiency, and the poor performance when multiple appliances are simultaneously operated. In this paper we contribute a hybrid, computationally efficient, algorithm for NILM, called Hybrid Signature-based Iterative Disaggregation (HSID), based on the combination of Factorial Hidden Markov Models, which provide an initial approximation of the end-use trajectories, and Iterative Subsequence Dynamic Time Warping, which processes the end-use trajectories in order to match the typical power consumption pattern of each appliance. In order to deal with the challenges posed by intrusive training, a supervised version of the algorithm, requiring appliance-level measurements for calibration, and a semi-supervised version, retrieving appliance-level information from the aggregate smart-metered signal, are proposed. Both versions are demonstrated onto a real-world power consumption dataset comprising five different appliances potentially operated simultaneously. Results show that HSID is able to accurately disaggregate the power consumption measured from a single-point smart meter, thus providing a detailed characterization of the consumers’ behavior in terms of power consumption. Numerical results also demonstrate that HSID is robust with respect to noisy signals and scalable to dataset including a large set of appliances. Finally, the algorithm can be successfully used in non-intrusive experiments without requiring appliance-level measurements, ultimately opening up new opportunities to foster the deployment of large-scale smart metering networks, as well as the design and practical implementation of personalized demand management strategies.