Growing needs for precise manipulation in medical surgery, manufacturing automation and structural health monitoring have motivated development of high accuracy, bandwidth and cost-effective sensing systems. Among these is a class of multi-axis electromagnetic devices where embedded magnetic fields can be capitalized for compact position estimation eliminating unwanted friction, stiction and inertia arising from dedicated and separate sensing mechanisms. Using fields for position measurements, however, is a challenging 'inverse problem' since they are often modeled in the 'forward' sense and their inverse solutions are often highly non-linear and non-unique. A general method to design a multisensor system that capitalizes on the existing magnetic field in permanent magnet (PM) actuators is presented. This method takes advantage of the structural field symmetry and meticulous placement of sensors to discretize the motion range of a PM-based device into smaller magnetic field segments, thereby reducing the required characterization domain. Within these localized segments, unique field-position correspondence is induced using field measurements from a network of multiple-axis sensors. A direct mapping approach utilizing trained artificial neural networks to attain multi-DOF positional information from distributed field measurements is employed as an alternative to existing computationally intensive model based methods which are unsuitable for real-time control implementation. Validation and evaluation of this technique are performed through field simulations and experimental investigation on an electromagnetic spherical actuator. An inclinometer was used as a performance comparison and experimental results have corroborated the superior tracking ability of the field-based sensing system. While the immediate application is field-based orientation determination of an electromagnetic actuator, it is expected that the design method can be extended to develop other sensing systems that harnesses other scalar, vector and tensor fields.