A classification method based on support vector machine and correlation imaging is proposed to address the problem of unknown object recognition. The method utilizes linear discriminant analysis to extract feature vectors from the objects. Based on these feature vectors, the characteristic speckle patterns are designed and applied to a correlation imaging system. By illuminating the objects with the characteristic speckle patterns, the bucket detector values are obtained from the correlation imaging system. The support vector machine is then employed to discriminate and classify the objects based on these bucket detector values. The feasibility of this approach is validated on the MNIST dataset. The results demonstrate that high classification accuracies can be achieved by the proposed method in all ten classification tasks, with an average classification accuracy of 90.5%. The comparison results with other classification methods indicate that the proposed method has more advantages in accuracy.