Ancient Indian and Chinese medicine both use non-invasive wrist pulse signals for health diagnosis of patients. In this paper, data obtained from a number of patients have been used to categorize the types of pulse signals that are found in both normal and abnormal health conditions. Features were extracted from the pulse signals using both frequency and wavelet transformations and these were then ranked according to their classification power for multiclass classifier design. Linear and quadratic pulse classifiers are proposed with raw features as well as subset of ranked features. Linear classifier has found to be giving highest classification accuracy of 73.82% using 4 ranked features.