AbstractError-tolerant recognition enables the recognition of strings that deviate slightly from any string in the regular set recognized by the underlying finite state recognizer. In the context of natural language processing, it has applications in error-tolerant morphological analysis, and spelling correction. After a description of the concepts and algorithms involved, we give examples from these two applications: In morphological analysis, error-tolerant recognition allows misspelled input word forms to be corrected, and morphologically analyzed concurrently. The algorithm can be applied to the moiphological analysis of any language whose morphology is fully captured by a single (and possibly very large) finite state transducer, regardless of the word formation processes (such as agglutination or productive compounding) and morphographemic phenomena involved. We present an application to error tolerant analysis of agglutinative morphology of Turkish words. In spelling correction, error-tolerant recognition can be used to enumerate correct candidate forms from a given misspelled string within a certain edit distance. It can be applied to any language whose morphology is fully described by a finite state transducer, or with a word list comprising all inflected forms with very large word lists of root and inflected forms (some containing well over 200,000 forms), generating all candidate solutions within 10 to 45 milliseconds (with edit distance 1) on a SparcStation 10/41. For spelling correction in Turkish, error-tolerant recognition operating with a (circular) recognizer of Turkish words (with about 29,000 states and 119,000 transitions) can generate all candidate words in less than 20 milliseconds (with edit distance 1). Spelling correction using a recognizer constructed from a large word German list that simulates compounding, also indicates that the approach is applicable in such cases.