Periodical manual calibrations assure the correct operation of an instrument for a certain period of time, but do not guarantee that faulty sensors remain calibrated for other periods, besides being occasionally unnecessary. In industrial plants the analysis of signals from sensors that monitor a plant is a difficult task due to the high-dimensionality of data. Therefore an online strategy for monitoring and correcting multiple sensors is required. This work proposes the use of two models: one that employs Auto Associative Neural Networks with a Modified Robust Training, and another that employs the Sequential Probability Ratio Test (SPRT) and Fuzzy Logic. These models are applied to an online monitoring system for self-correction and selfvalidation of measurements generated by a large number of sensors. Unlike existing models, the proposed system makes use of only one AANN to reconstruct signals from faulty sensors. Moreover, the states of the sensor and of the self correction model are validated through the reliability and classification modules, respectively. The model is evaluated with a database containing measurements of industrial sensors that control and carry out the monitoring of an internal combustion engine installed in a mining truck. Experimental results show the ability of the proposed model to map and correct signals from faulty sensors with a low error, and to provide a tool for sensor review scheduling.