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This paper addresses a machine learning-based approach to the study of the effect of interference in single frequency networks (SFNs). The self-interference in overlapping areas is analyzed by assuming a dependency on the received signal parameters. For this purpose, an experimental assessment is performed for creating a database that relates the received signal parameters to the resultant signal quality metrics. The laboratory setup emulates an SFN scenario with two interfering transmitters. The main received signal electric-field strength and the relative values of attenuation and delay corresponding to the remaining transmitter constitute the inputs in our system. The main received signal modulation error ratio (MER), the resultant signal MER, and the SFN gain are the output parameters. The proposed neural network models offer predictions of the main received signal MER, the resultant signal MER, and the …