This paper proposes Normal Form Transformation (NFT) as a preprocessing of Support Vector Machines (SVMs). Object recognition from images can be regarded as a fundamental technique in discovery science. Aspect-based recognition with SVMs is effective under constrained situations. However, object recognition from rotated, shifted, magnified or reduced images is a difficult task for simple SVMs. In order to circumvent this problem, we propose NFT, which rotates an image based on low-luminance directed vector and shifts, magnifies or reduces the image based on the object’s maximum horizontal distance and maximum vertical distance. We have applied SVMs with NFT to a database of 7200 images concerning 100 different objects. The recognition rates were over 97% in these experiments except for cases of extreme reduction. These results clearly demonstrate the effectiveness of the proposed approach in aspect-based recognition.