This paper is the first attempt to investigate the performance of different learning rules in fitting survey data of household and expert inflation expectations in five core European economies (France, Germany, Italy, Netherlands and Spain). Overall it is found that constant gain learning performs well in out-of-sample forecasting. It is also shown that households in high inflation countries are using higher best fitting constant gain parameters than those in low inflation countries. They are hence able to pick up structural changes faster. Professional forecasters update their information sets more frequently than households. Furthermore, household expectations in the Euro Area have not converged to the inflation goal of the ECB, which is to keep inflation below to but close to 2% in the medium run. This contrasts the findings for professional experts, which seem to be more inclined to incorporate the implications of monetary union for the convergence in inflation rates into their expectations.