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Artificial neural network (ANN) based inverse modeling technique is used for sensor response linearization. The choice of the order of the model and the number of the calibration points are important design parameters in this technique. An intensive study of the effect of the order of the model and number of calibration points on the lowest asymptotic root-mean-square (RMS) error has been reported in this paper. Starting from the initial value of the nonlinearity in the characteristics of a sensor and required RMS error, it is possible to quickly fix the order of the model and the number of calibration points required using results of this paper. The number of epochs needed to calibrate the sensor, and thereafter the epochs needed to recalibrate in event of sensitivity or offset drifts, are also presented to bring out the convergence time of the technique. More importantly, the advantages of the ANN technique over traditional regression based modeling are also discussed from the point of view of its advantage in hardware simplicity in microcontroller based implementation. Results presented in this paper would be of interest to instrumentation design engineers.