Sensors are widely used in industrial processes, automobiles, robotics, avionics and other systems to monitor and control the system behavior. Besides, the use of precise, accurate and low power sensors has recently emerged in many sensor network applications. Capacitive Sensors, because of their high sensitivity and low power consumption are extensively used in various applications to measure pressure, force, position, speed, acceleration, liquid level, dielectric properties and flow of material. However one of the drawbacks of capacitive sensor is the relative small change in capacitance of the sensor due to applied pressure is small, compared to the offset capacitance, and its response characteristics is highly nonlinear. Another problem associated in general, with all sensors is that, their response characteristics are influenced by the disturbing environmental parameters, e.g. temperature, humidity and pollution. For example, in the case of capacitive pressure sensor (CPS) its response depends not only on the applied pressure that also on the environmental temperature. This problem becomes severe, especially when the capacitive sensor is operated in a harsh environment where temperature variation is large. Usually, an exact mathematical model of a sensor showing the relationship between the measured and its response, and the dependency of sensor output on environmental parameters is not available. Furthermore, since most sensors exhibit some degrees of nonlinear response characteristics, and the environmental parameters influence the sensor behavior nonlinearly, the problem of obtaining an accurate readout and its calibration becomes highly complex. Some of the ideal properties of a sensor include linear response characteristics, auto-correction for the adverse effects of nonlinear environmental parameters, high sensitivity and accuracy, and low power consumption. However, in practical situation, it is not easy to achieve ideal sensor characteristics, especially when the sensor is operating in a harsh environment. The main objective of this research is to design an intelligent compensator which is capable of compensating adverse effect of environment temperature on cps .for this purpose in this research we design a benchmark which models the real condition that cps operates in. The temperature of this system in varied, in each constant temperature a differential pressure is applied to cps and out pit of cps is recorded. This process is being kept on for each separate temperature. By gathering these data, an artificial neural network is being trained to learn dependency of cps behavior to ambient temperature. After that this compensator is Key words: Capacitive Differential Pressure Sensors-Intelligent Compensator-Adaptive Neural fuzzy-Look up table