Process mining can be seen as the “missing link” between data mining and business process management. Although nowadays, in the context of process mining, process discovery attracts the lion’s share of attention, conformance checking is at least as important. Conformance checking techniques verify whether the observed behavior recorded in an event log matches a modeled behavior. This type of analysis is crucial, because often real process executions deviate from the predefined process models. Although there exist solid conformance checking techniques for procedural models, little work has been done to adequately support conformance checking for declarative models. Typically, traces are classified as fitting or non-fitting without providing any detailed diagnostics. This paper aligns event logs and declarative models, i.e., events in the log are related to activities in the model if possible. The alignment provides then sophisticated diagnostics that pinpoint where deviations occur and how severe they are. The approach has been implemented in ProM and has been evaluated using both synthetic logs and real-life logs from Dutch municipalities.