This paper presentsa soft sensor design technique for estimation of pitch and yaw angular positions of a Twin Rotor MIMO System (TRMS).The objective of the proposed workwas to calculate the value of pitch and yaw angular positions using a stochastic estimation technique. Methods:Measurements from optical sensorswereused to measure fan blade rotationsper minute(RPM). The Kalman filter,which is a stochastic estimator,was used in the proposed system and its resultswere compared with those oftheLuenbergerobserver and neural network. The TwinRotor MIMOSystem is a nonlinear system with significant cross coupling between its rotors. Results:The estimatorswere designed for the decoupled system andwere applied in real life to the coupled TRMS. The convergence of estimation to the actual valueswas checked on a practical setup. The Kalman filter estimatorswere evaluated for various inputs and disturbances, and the resultswere corroborated in real time. Conclusion:From the proposed work itwas seen thattheKalman filterhadatleastIntegral Absolute Error(IAE),Integral Square Error(ISE),Integral Time Absolute Error(ITAE)as compared totheneural network andtheLuenbergerbased observer.