This paper make use of PCA for sensor fault identification via reconstruction. The principal component model captures the measurement correlations and reconstructs each variable to define associated residuals and a Sensor Validity Index (SVI). The filter applied to the SVI adds an important feature for sensor fault isolation because reduces the effect of false alarms and allows the identification of different types of sensor faults.