In this paper we used clustering algorithms to compare the typical load profiles of different European countries in different day of the weeks. We find out that better results are obtained if the clustering is not performed directly on the data, but on some features extracted from the data. Clustering results can be exploited by energy providers to tailor more attractive time-varying tariffs for their customers. In particular, despite the relevant differences among the several compared countries, we obtained the interesting result of indentifying a single feature that is able to distinguish weekdays from holidays and pre-holidays in all the examined countries.