Real-world natural language sentences are long and complex, and always contain unexpected grammatical constructions. It even includes noise and ungrammaticality. This paper describes the Controlled Skip Parser, a program that parses such real-world sentences by skipping some of the words in the sentence. The new feature of this parser is that it can control its behavior to find out which words to skip, without using domain-specific knowledge. Statistical information (N-grams), which is a generalized approximation of the grammar learned from past successful experiences, is used for the controlled skip. Experiments on real newspaper articles are shown, and our experience with this parser in a machine translation system is described.