This paper presents the development of an artificial neural network (ANN)-based improved inductive signal conditioning circuit for pressure transducer with bellow as sensor. A bellow is an elastic-type mechanical pressure sensor. The deflection of the bellow due to applied pressure must be translated into an efficient electrical signal for continuous monitoring, wireless transmission, and digital readout. A ferromagnetic wire attached to the bellow as a part of an inductive coil arrangement, gets deflected due to applied pressure, thereby changing the self inductance of the coil. An op-amp inductive signal conditioning circuit (OISCC) is designed to produce voltage proportional to changes in self-inductance, but the OISCC voltage versus applied pressure exhibits a considerable nonlinearity error due to stray inductances and component drifts. The ANN modeling estimates and compensates the nonlinearity of OISCC. An embedded unit is used for implementation of ANN learning process. The pressure transducer with significant stability has exhibited high linearity and sensitivity of +/- 0.35% and 10 mH/psig, respectively, in the measuring range of 0-70 psig. The design and experimental aspects of the technique are reported.