Characterizing record labels and their sound signatures is a difficult problem, especially when it comes to indie record labels. That's the present when dealing with record labels of electronic music, so the application of different constructs and techniques that have been proved useful when looking at record labels is essential. On top of that Electronic music increases the challenges in similar issues, as timbre and rhythm are more important than root keys, chords, which are facets traditionally emphasized in music information re- trieval. The research presented in this dissertation aims at the ex- ploration of the usage of Music Information Retrieval tools and tech- niques for the analysis of Electronic Music Record Label based on audio content. For that purpose we have curated a music collection especially addressed for the above-mentioned problems, containing more than 3000 tracks of 9 different Electronic Music Record Label. The collection has been analyzed with the help of Essentia's library, and the extracted features present various musical criteria such as timbre, rhythm, and tonality. The extracted features are tested with different classication algorithms, from the simplest of them, a Support Vector Machine, to a Fully Connected layer network, to a more complex one, a Convolutional Neural Network. We also propose various ways of segmenting Electronic Music, in order to capture the most relevant features. We tried to cover as much types of segments as we can, which were tested experimentally, achieving satisfactory results. Finally, a detailed qualitative analysis of the results obtained when considering a group of record labels is per- formed, demonstrating the potential of the analysis that have been developed.