In this paper, we described an algorithm of automatic detection of Ground Glass Opacities (GGO) from X-ray CT images. In this algorithm, first, suspicious shadows are extracted by our Variable N-Quoit (VNQ) filter which is a type of Mathematical Morphology filters. This filter can detect abnormal shadows with high sensitivity. Next, the suspicious shadows are classified into a certain number of classes using feature values calculated from the suspicious shadows. In our traditional clustering method, a medical doctor has to manually classify the suspicious shadows into 5 clusters. The manual classification is very hard for the doctor. Thus, in this paper, we propose a new automatic clustering method which is based on a Principal Component (PC) theory. In this method, first, the detected shadows are classified into two sub-clusters according to their sizes. And then, each sub-cluster is further classified into two sub-sub-clusters according to PC Scores(PCS) calcuated from the feature values of the shadows in the sub-cluster. In this PCS-based classification, we use a threshold which maximizes the distance between the two sub-sub-clusters. The PCS-based classification is iterated recursively. Using discriminate functions based on Mahalanobis distance, the suspicious shadows are determined to be normal or abnormal. This method was examined by many samples (including GGO's shadows) of chest CT images, and proved to be very effective.