HUMAN mind has a limited capability for processing a huge amount of detailed information in his environment; thus, to compensate, the brain groups together the information it perceives by its similarity, proximity, or functionality and assigns to each group a name or a “word” in natural language. This classification of information allows human to perform complex tasks and make intelligent decisions in an inherently vague and imprecise environment without any measurements or computation. Inspired by this human capability, Zadeh introduced the machinery of CW as a tool to formulate human reasoning with perceptions drawn from natural language and argued that the addition of CW theory to the existing tools gives rise to the theories with enhanced capabilities to deal with real-world problems and makes it possible to design systems with higher level of machine intelligence . To do this, CW offers two principal components, (1) a language for representing the meaning of words taken from natural language, this language is called the Generalized Constraint Language (GCL), and (2) a set of deduction rules for computing and reasoning with words instead of numbers. CW is rooted in fuzzy logic; however, it offers a much more general methodology for fusion of natural language propositions and computation with fuzzy variables. CW inference rules are drawn from various fuzzy domains, such as fuzzy logic, fuzzy arithmetic, fuzzy probability, and fuzzy syllogism. This paper reports a preliminary work on the implementation of a CW inference system on top of JESS expert system shell (CWJess) . The CW reasoning is fully integrated with JESS facts and inference engine and allows knowledge to be specified in terms of GCL assertions.