This paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decompose the aggregated energy load from households in events and algorithms based on expert knowledge to assign each of these events to four types of appliances: fridge, dishwasher, microwave, and washer/dryer. The methodology was developed to work with smart meters that have a granularity of 1 min and was evaluated using the Reference Energy Disaggregation Dataset. The results show that the algorithm can disaggregate the refrigerator with high accuracy and the usefulness of the proposed methodology to extract relevant features from other appliances, such as the power use and duration from the heating cycles of a dishwasher.