The Electric Network Frequency (ENF) is the supply frequency of power distribution networks, which can be captured by multimedia signals recorded near electrical activities. It normally fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The ENF remain consistent across the entire power grid. This has led to the emergence of multiple forensic application like estimating the recording location and validating the time of recording. Recently an ENF based Machine Learning system was proposed which infers the region of recording from the power grids ENF signal extracted from the recorded multimedia signal, with the help of relevant features. In this work, we report some features novel to this application which serve as identifying characteristics for detecting the region of origin of the multimedia recording. In addition to the ENF variation itself, the utilization of the ENF harmonics pattern extracted from the media signals as novel features enables a more accurate identification of the region of origin. These characteristics were used in a multiclass machine learning implementation to identify the grid of the recorded signal.