  In this paper, we describe an approach to sentence categorization which has
the originality to be based on natural properties of languages with no training
set dependency. The implementation is fast, small, robust and textual errors
tolerant. Tested for french, english, spanish and german discrimination, the
system gives very interesting results, achieving in one test 99.4% correct
assignments on real sentences.
  The resolution power is based on grammatical words (not the most common
words) and alphabet. Having the grammatical words and the alphabet of each
language at its disposal, the system computes for each of them its likelihood
to be selected. The name of the language having the optimum likelihood will tag
the sentence --- but non resolved ambiguities will be maintained. We will
discuss the reasons which lead us to use these linguistic facts and present
several directions to improve the system's classification performance.
  Categorization sentences with linguistic properties shows that difficult
problems have sometimes simple solutions.
