A machine that can read unconstrained handwritten words remains an unsolved problem. For example, automatic entry of handwritten documents into a computer is yet to be accomplished. Most systems attempt to segment letters of a word and read words one character at a time. Segmenting a handwritten word is very difficult and often, the confidence of the results is low. Another method which avoids segmentation altogether is to treat each word as a whole. This research investigates the use of Fourier Transform coefficients, computed from the whole word, for the recognition of handwritten words. To test this concept, the particular pattern recognition problem studied consisted of classifying four handwritten words, `Buffalo', `Vegas', `Washington', and `City' from the SUNY post office database. Several feature subsets of the Fourier coefficients are examined. The best recognition performance of 76.2% was achieved when the Karhunen-Loeve transform was computed on the Fourier coefficients and those features were fed into a multilayer perceptron.