In recent years, it is becoming increasingly difficult to ignore the impact of Web robots on both commercial and institutional Web sites. Not only do Web robots consume valuable bandwidth and Web server resources, they are also making it more difficult to apply Web Mining techniques effectively on the Web logs. E-commerce Web sites are also concern about unauthorized deployment of shopbots for the purpose of gathering business intelligence at their Web sites. Ethical robots can be easily detected because they tend to follow most of the guidelines proposed for robot designers. On the other hand, unethical robots are more difficult to identify since they tend to camouflage their entries in the Web logs. In this paper, we examine the problem of identifying navigational patterns of Web robots using conventional machine learning techniques. Our goal is to construct a predictive model that will distinguish between the browsing behavior of legitimate Web users from access patterns due to Web robots. Our results show that highly accurate models can be obtained using a small set of access features deduced from the Web logs.