In chemical plants, soft sensors are used to predict difficult-to-measure process variables. Soft sensor models must adapt to process changes by using new measured data. However, when a model is reconstructed with data that have low variation, the model cannot predict abrupt changes of process characteristics. The predictive performance of adaptive models depends on databases. We therefore propose an index to monitor database, that is, database monitoring index (DMI), and a database monitoring method using the DMI. The DMI is based on similarity between two data. The more similar two data are the smaller value the DMI has. New data are stored when the minimum DMI-value of the data exceeds a threshold. Through the analysis of simulation data and real industrial data, we confirmed that databases can be appropriately managed and the predictive accuracy of adaptive soft sensor models increased by using the proposed method. (c) 2013 American Institute of Chemical Engineers AIChE J, 60: 160-169, 2014