This article proposes a new approach for etch endpoint detection of small open area wafers. The traditional endpoint detection technique uses a few manually selected wavelengths, which are adequate for large open areas. As the integrated circuit devices continue to shrink in geometry and increase in device density, detecting the endpoint for small open areas presents a serious challenge to process engineers. In this work, a high-resolution optical emission spectroscopy (OES) system is used to provide the necessary sensitivity for detecting subtle endpoint signals. Principal component analysis is used to analyze the OES data and extract key components that capture the endpoint signal. Data analysis from many wafers shows that the endpoint pattern in the principal components is repeatable. Two methods are used to select wavelengths so as to improve reliability sind reduce susceptibility to noise. The first method is to remove those spectrum windows that contain no endpoint information. The second method is to use a "sphere" criterion to select the most relevant wavelengths. The final endpoint algorithm using a much-reduced number of wavelengths shows more distinguishable and reliable endpoint features.