The introduction of a foreign object into the human body may result from different processes ranging from iatrogenic events during surgical procedures to traumatic events caused by accidents or violence such as firing and piercing with sharp objects. The presence of these foreign bodies within the human body can cause health problems ranging from pain and discomfort to death. For surgical removal, it is essential to locate these objects, with high accuracy, to reduce surgical time and guarantee the success of the procedure. The objective of the present work is to contribute to the study of the localization problem of magnetic foreign bodies (intrinsically magnetic or by external magnetic induction) in the human body. The developed computational algorithms are capable of promoting simulations of magnetic flux density patterns in a plane from an extensive source, represented by modifiable parameters of length, spatial position and slope to the measurement plane. These simulations were used as input for the training of Artificial Neural Networks that, after training, were able to solve the inverse problem, characterizing, from the magnetic field map, the spatial position, size (length) and slope of the metallic source. The results indicated a better performance with the use of triaxial sensors, whose mean square error, in 3640 tests, was less than 2 mm in the spatial orientation, 8 mm in length and 17 Celsius degrees for the tilt of the magnetic source in relation to the measurement plane.