A novel Markov random field (MRF) based framework is developed for the problem of 3D object recognition in multiple statuses. This approach utilizes densely sampled grids to represent the local information of the input images. Markov random field models are then created to model the geometric distribution of the object key points. Flexible matching, which seeks to find an accurate correspondence mapping between the key points of two images, is performed by combining the local similarities with the geometric relations using the highest confidence first (HCF) method. Afterwards, similarities between different images are calculated for object recognition. The algorithm is evaluated using the Coil-100 object database. The excellent recognition rates achieved in all the experiments indicate that our approach is well-suited for appearance-based 3-D object recognition. Comparisons with previous methods show that the proposed one is far more robust in the presence of object zooming, rotation, occlusion, noise, and viewpoint variations.