To improve magnetic disturbance rejection and robustness of wellbore survey measurements, an adaptive neuro network-based fuzzy inference system (ANFIS) filter for wellbore position calculation is presented. This technique significantly improves magnetic disturbance rejection and reduces sensor error influence for borehole survey measurements. The new approach for the ANFIS filter is based on two redundant sets of IMUs which are located in different positions in the BHA at a known, constant distance. The distance between these two sets of IMUs will physically fade the effect of the magnetic disturbances. Each IMU set outputs position estimation based on the splines method which is then input into an ANFIS filter. The inputs of the splines calculation are azimuth, inclination angles and measurement depth, and the outputs are moving distance in three directions (Northing, Easting and True Vertical Depth). However, the accuracy of the splines method highly depends on the accuracy of the inputs, which are difficult to obtain during the measurement while drilling process even under pure clean environments (without any magnetic disturbances). Furthermore, the distorted azimuth caused by magnetic interference affects the borehole position accuracy. In order to deal with those problems, the designed ANFIS filter has a two-level structure. First a local level position estimation (splines method or well trained local ANFIS based on the sensor accuracy) for two sensor sets is used. If the sensor measurement accuracy is low, this local ANFIS will correct the position estimation. Then the outputs of the local modules were input into ANFIS for second level filtering (global filter) to remove the error which caused by unknown magnetic disturbances. According to the judgement of the ANFIS, the IMU set with the smaller magnetic disturbance is given greater weight to reduce the interference effect on the borehole position estimation. This two-level filter is compared to the traditional splines method under different tests situations. First, we evaluate this method by comparing with GPS positioning, from this test we know that the ANFIS filter shows a good performance when the magnitude of magnetic disturbance is within the training magnitude range. Even when the magnitude of magnetic disturbance is above the training range, the ANFIS filter shows a higher robustness than the traditional splines method. Also, this method was applied to borehole data with two IMU containing accelerometers and one magnetometer measurements. In order to apply our method, we duplicated one more magnetometer measurement data under magnetic interference for assessment. The results proved its magnetic disturbance robustness in borehole position estimation. Finally, we demonstrate the full potential using a laboratory experimental setup.