Recent work in machine learning for information extraction has focused on two distinct sub-problems: the conventional problem of filling template slots from natural language text, and the problem of wrapper induction, learning simple extraction procedures ("wrappers") for highly structured text such as Web pages. For suitable regular domains, existing wrapper induction algorithms can efficiently learn wrappers that are simple and highly accurate, but the regularity bias of these algorithms makes them unsuitable for most conventional information extraction tasks. This paper describes a new approach for wrapping semistructured Web pages. The wrapper is capable of learning how to extract relevant information from Web resources on the basis of user supplied examples. It is based on inductive learning techniques as well as fuzzy logic rules. Experimental results show that our approach achieves noticeably better precision and recall coefficient performance measures than SoftMealy, which is one of the most recently reported wrappers capable of wrapping semi-structured Web pages with missing attributes, multiple attributes, variant attribute permutations, exceptions, and typos.