In this paper, we experimentally evaluate the validity of dimension-reduction methods which preserve topology for image pattern recognition. Image pattern recognition uses pattern recognition techniques for the classification of image data. For the numerical achievement of image pattern recognition techniques, images are sampled using an array of pixels. This sampling procedure derives vectors in a higher-dimensional metric space from image patterns. For the accurate achievement of pattern recognition techniques, the dimension reduction of data vectors is an essential methodology, since the time and space complexities of data processing depend on the dimension of data. However, the dimension reduction causes information loss of geometrical and topological features of image patterns. The desired dimension-reduction method selects an appropriate low-dimensional subspace that preserves the topological information of the classification space.