This paper studies the means of detecting steel strip surface defects produced by a hot strip mill downcoiler. The analysis is based on the force feedback signals taken from the side-guide and the pinch-roll of the steel mill. The study compares the results from two approaches. One approach is to apply principal component analysis (PCA) on the autoregressive (AR) model spectrum, and the other is to apply PCA on the signal periodogram. The spectrum estimation processes help eliminate the noise effects, and the PCA then distinguishes the signals into different clusters. Some of these clusters will then represent the problematic strips. The analysis results on the data from China Steel Corporation (CSC) show that PCA in conjunction with the AR model is able to separate a major portion of the defective processes, and that PCA with the periodogram is not as effective. However, the two processes sometimes compensate for each other, and the combination of the methods may provide improved results.