Geomagnetic information is available over much of the Earth. Geomagnetic navigation based on neural networks (NNs) is challenging because all measurement vectors mapping to the positions on the reference map should be classified in advance, and the measurements for mapping are highly non-linear. This approach fails to map positions when measurements that have not been pre-classified in the new area are input. It limits the navigation area because it is hard to assign all positions on the reference map to classes. In this study, the authors present a new approach combining two symmetric convolutional NNs (CNNs) and normalised cross-correlation (NCC). Two symmetric CNNs trained to find similarity are used to find candidate regions in a search area. Then NCC is applied to find a matching position. This approach enlarges the geomagnetic navigation area regardless of training, and it enables processing even if geomagnetic measurements are acquired in a new area. The results of the numerical simulation indicate that the mean matching rate is over 98.6% for the best and worst geomagnetic profile. Furthermore, they show that the algorithm can be applied for initial position estimation in a search area by showing improvement of convergence time and position estimation error.