Image correlation is often required to utilize the complementary information in CT, MRI, and SPECT. A practical method for automatic image correlation in three-dimensions (3D) based on chamfer matching is described. The method starts with automatic extraction of contour points in one modality and automatic segmentation of the corresponding feature in the other modality. A distance transform is applied to the segmented volume and a cost function is defined that operates between the contour points and the distance transform. Matching is performed by iteratively optimizing the cost function for 3D translation, rotation, and scaling of the contour points. The complete matching process including segmentation requires no user interaction and takes about 100 s on an HP715/50 workstation. Perturbation tests on clinical data with cost functions based on mean, rms, and maximum distances in combination with two general purpose optimization procedures have been performed. The performance of the methods has been quantified in terms of accuracy, capture range, and reliability. The best results on clinical data are obtained with the cost function based on the mean distance and the simplex optimization method. The accuracy is 0.3 mm for CT-CT, 1.0 mm for CT-MRI, and 0.7 mm for CT-SPECT correlation of the head. The accuracy is usually at subpixel level but is limited by global geometric distortions, e.g., for CT-MRI correlation. Both for CT-CT and CT-MRI correlation the capture range is about 6 cm, which is higher than normal differences in patient setup found on the scanners (less than 4 cm). This means that the correlation procedure seldom fails (better than 98% reliability) and user interaction is unnecessary. For CT-SPECT matching the capture range is about 3 cm (80% reliability), and must be further improved. The method has already been introduced in clinical practice.