Inferential sensors, or soft sensors, refer to a modeling approach to estimating hard-to-measure process variables from other easy-to-measure, on-line sensors. Since many sensors are used as input variables to estimate the output, the probability that one of the sensors fails increases significantly. In this paper, we propose a self-validating inferential sensor approach based on principal component analysis (PCA). The input sensors are validated using a fault identification and reconstruction approach proposed in Dunia et al. AlChE J. 1996, 42, 2797-2812. A principal component model is built for the input sensors for sensor validation, and the validated principal components are used to predict output variables using linear regression or neural networks. If a sensor fails, the sensor is identified and reconstructed with the best estimate from its correlation to other sensors. The principal components are also reconstructed accordingly for prediction. The number of principal components used in sensor validation and prediction are chosen differently based on different criteria. The typical input correlation or collinearity is utilized for sensor validation and removed in predicting the output to avoid ill-conditioning. The self-validating soft sensor approach is applied to air emission monitoring, where continuous monitoring of the air pollutants is required for environmental regulations.