In this article we address the issue of shape estimation using electric sense inspired by the active electric fish. These fish can perceive their environment by measuring the perturbations in a self-generated electric field caused by nearby objects. The approach proceeded in three stages. Firstly the object was detected and its electric properties (insulator or conductor) identified. Secondly, the object was localized using the MUSIC (MUltiple SIgnal Classification) algorithm, which was originally developed to localize a radio wave emitter using a network of antennas. Thirdly, the shape estimation relied on the concept of generalized polarization tensor (GPT), which enabled modeling the electric response of an object polarized by an ambient electric field. We describe the implementation of the approach through numerous experiments. The system was able to estimate shape with an average error of 16%, and opened the way toward further improvements. In particular, self aligning the sensor with the ellipsoid through a reactive feedback makes the shape estimation errors drop to 10%.