Various methods based on extended Kalman filter, adaptive fading Kalman filter and steady state Kalman filter have been presented for inferential estimation of compositions from temperature measurements in multiple fraction multicomponent batch distillation. This dynamic model based state estimators incorporate component balance equations together with thermodynamic relations, that include bubble point temperature computation and avoid constant relative volatility concept. A performance criterion with multiple performance indices is used to assess the composition estimators. The sensitivity of the estimators is studied with respect to the effect of number of measurements, measurement noise, and filter design parameters including initial state covariance, process and observation noise covariance matrices. The results of the state estimation methods are evaluated by applying them for composition estimation on all trays, reboiler, condenser and products of a ternary hydrocarbon system. The results show the better suitability of the method of extended Kalman filter for composition estimation in multicomponent batch distillation.