In this paper, a system for recognition of machine printed Gurmukhi script is presented. Research in the field of Character recognition of Gurmukhi script faces major problems mainly related to the unique characteristic of the script like connectivity of characters on the headline, a large number of similar characters and two or more characters in a word having intersecting minimum bounding rectangles. The recognition system presented in this paper operates at sub-character level. The segmentation process breaks a word into sub-characters and the recognition phase consists of classifying these sub-characters and combining them to form Gurmukhi characters. A set of very simple and easy to compute features is used and a hybrid classification scheme consisting of binary decision trees and nearest neighbors is employed. A recognition rate of 96.6% at the processing speed of 175 characters/second was achieved on clean images of text without employing any post-processing technique.