An MT-oriented system using Conditional Random Fields (CRFs) is presented to identify English Prepositional Phrases (PPs) within business domain. For the purpose of English-Chinese Machine Translation (MT), we, under the guidance of the theory of Syntactic Functional Grammar (SFG), refine PP function chunks into four types instead of the binary attachment. In order to improve the identification of these chunk types, we revise the Penn Treebank tagset with four major changes being made. A small size of 998k English annotated corpus in business domain is semi-automatically built based on our new tagset employing the Maximum Entropy model. Experiments show that our system achieves an accuracy of 88.45%, higher than other reported approaches. The adjustments made in the PP chunk types and POS tagset give rise to 4.11%, 4.25% and 4.15% increase in the precision, recall and F-score respectively.