Electrical impedance tomography (EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution or change by making voltage and current measurements on the object’s periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method deciding the place of impedance change for EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface, and then the impedance change will be reconstructed with linear approximated method. MBPNN can decide the impedance change location exactly without needing long training time. It alleviates some noise affection and can be expanded, which makes sure about the high precision and space resolution of the reconstructed image that can’t be accessed by the back projection method.