Principal component analysis (PCA) was modified for real-time applications and applied to the end-point detection of small open area SiO2 plasma etching. Typically, the end point of plasma etching is determined from a few manually selected wavelengths. Determining the end point of the plasma etching using this approach is quite a challenge when the exposed open area is less than several percent. To increase the sensitivity, information was extracted from the entire spectra of 2755 signals in the range of 200-1100 nm, using a PCA algorithm. In this study, the PCA algorithm was modified to allow real-time applications of end-point detection. The loading vector was determined from the model wafer, and the score vector was determined using the real-time data of the target wafer to reduce the processing time. This algorithm was tested for the small open area Of SiO2 etching of a 200 ms sampling period, using the entire optical emission spectra, through a comparison with a defined signal-to-noise ratio. The results were compared with the conventional single wavelength signals of SiF (440.2 nm), CO (482.5 nm), and Si (505.6 nm). The end-point detection of 0.4%-0.8% SiO2 open area was achieved using the suggested algorithm, while the single wavelength showed limitations in the open areas above a few percent. The sensitivity was also increased by a factor of 2.15, compared to the signal-to-noise ratio of the single wavelength method.