This paper summarizes the three robust feature detection methods: Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)–SIFT and Speeded Up Robust Features (SURF). This paper uses KNN (K-Nearest Neighbor) and Random Sample Consensus (RANSAC) to the three methods in order to analyze the results of the methods’ application in recognition. KNN is used to find the matches, and RANSAC to reject inconsistent matches from which the inliers can take as correct matches. The performance of the robust feature detection methods are compared for scale changes, rotation, blur, illumination changes and affine transformations. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although it’s slow. SURF is the fastest one with good performance as the same as SIFT. PCA-SIFT show its advantages in rotation and illumination changes.