Identifying a VoIP call as SPAM based on call characteristics is an important issue that has never been studied before. Most of the studies of VoIP SPAM impose the whole burden on the callee to judge SPAM calls. In other words, the accuracy of the identification process is totally based on the callee identifying the call as SPAM, which is questionable and not reliable. In this paper, a two-stage VoIP SPAM identification framework is introduced. The first stage is a pre-call identification process, which uses a set of parameters about the call that can be collected before allowing the call to go through. The second stage is a post-call identification process that uses other parameters that can be collected during/after the call. The first stage provides a pre-call evaluation score of the call, while the second stage further tunes this score. In the proposed framework, the decision of identifying VoIP SPAM calls is based on several uncertain parameters that represent meta-data of VoIP calls. These parameters include call duration, amount of exchanged information in each direction, and calling pattern. In this study, the potential set of parameters that can be used to identify VoIP SPAM are investigated. A set of rules is used in addition to any prior evaluation of the caller to provide the pre-call score. Then, a fuzzy-logic controller is developed to identify VoIP SPAM in the second stage. An augmented ongoing tuning strategy is adopted where callee feedback, if any, is taken into account to further tune the identification process. Simulation studies are carried out to demonstrate the effectiveness of the two-stage approach in identifying VoIP SPAM based on the proposed framework.