A new system for the recognition of handwritten text is described. The system goes from raw, binary scanned images of census forms to {ASCII} transcriptions of the fields contained within the forms. The first step is to locate and extract the handwritten input from the forms. Then, a large number of character subimages are extracted and individually classified using a {MLP} ({M}ulti-{L}ayer {P}erceptron). A {V}iterbi-like algorithm is used to assemble the individual classified character subimages into optimal interpretations of an input string, taking into account both the quality of the overall segmentation and the degree to which each character subimage of the segmentation matches a character model. The system uses two different statistical language models, one based on a phrase dictionary and the other based on a simple word grammar. Hypotheses from recognition based on each leanguage model are integrated using a decision tree classifier. Results from the application of the system to the recognition of handwritten responses on {U.S.} census forms are reported.