Abstract Several applications including text-to-speech require some normalized format of non-standard words in various domains such as numbers, dates, and currencies and in various human languages. The traditional approach of manually constructing a program for such a normalization task requires expertise in both programming and target (human) language and further does not scale to a large number of domain, format, and target language combinations. We propose to learn programs for such normalization tasks through examples. We present a domainspecific programming language that offers appropriate abstractions for succinctly describing such normalization tasks, and then present a novel search algorithm that can effectively learn programs in this language from input-output examples. We also briefly describe domain-specific heuristics for guiding users of our system to provide representative examples for normalization tasks related to that domain. Our experiments show that we are able to effectively learn desired programs for a variety of normalization tasks.