The viability of Near Infra Red (NIR) Spectrometry for internal quality assessment in fruit and vegetables is accepted world wide even for real-time applications. However, the transfer of technology to the agroindustry is still a challenge due to a high number of uncontrolled sources of variation which modify the spectral information, and reduce the accuracy of estimations. Some of these sources of variation are: the internal temperature of the product and the spectrometer (Hernández-Sánchez et al., 2003), the skin thickness (Krivoshiev et al., 2000), and the presence of boundary layers and voids inside the product (Fraser et al., 2003).A main issue when developing a new NIR application is selection of the interaction mode between the light and the sample: reflectance, interactance or transmittance. The interactance mode, though it is the most difficult situation for online implementation, has shown encouraging results for obtaining good predictive models (Schaare and Fraser, 2000). AbstractThe transfer of NIR spectroscopy to industry relies on the possibility of real time identification of abnormal spectra as well as uncontrolled sources of variation. This study proposes an unsupervised procedure for the identification under an industrial application of daily events (general changes) and abnormal observations. It consists in defining a spectral database at the beginning of a season, performing a principal component (PC) analysis, and calculating the PC scores over time. Process control statistics (Hotelling T 2 , Q) are used for multivariate supervision of the industrial application. Within this procedure 10,400 average spectra of onion bulbs were evaluated identifying events in 12 out of 66 work dates, as well as spectral trends throughout the season of 2002.