Abstract: Complex event processing is a data analysis technology which is widely applied in time-critical applications such as RFID-enabled object tracking, stock trend prediction and network intrusion detection, etc. In an RFID-enabled monitoring system, RFID objects are always tracked with complex event queries. Existing RFID complex event processing techniques mainly focus on event detection and optimizations over single RFID object. However, in many RFID scenarios (such as in an RFID-enabled office or auto assembly line), complex events sequences of multiple co-located and correlated objects are always subscribed due to consistency checking and regularity requirements. In this paper, event processing issues over multiple correlated RFID objects are investigated. To support multiple correlated objects event query definition, semantics of existing event operators is extended. With pattern transformation rules, non-linear multi-objects patterns are transformed into linear multi-objects patterns which can be well evaluated within a unified evaluation framework. A multiple co-related objects complex event query evaluation model called NFA\-b2 and the corresponding event detection algorithms are proposed. By pushing check-point constraint check down and postponing co-location constraints in event detection process, unviable runtime instances and pattern matching search space are greatly reduced which can conserve huge CPU time. Empirical experimental analyses between the proposed multiple RFID objects event detection algorithm and the slightly altered popular event detection algorithm—SASE illustrate both the efficiency and soundness of the proposed algorithm.