Conventional　statistical　process　control　tools　target　either　continuous　or　categorical　data　but　seldom　both　at　the　same　time.　However,　mixed-type　data　consisting　of　both　continuous　and　categorical　observations　are　becoming　more　common　in　modern　manufacturing　processes　and　service　management.　However,　they　cannot　be　analyzed　using　traditional　methods.　By　assuming　that　there　is　a　latent　continuous　variable　that　determines　the　attribute　levels　of　a　categorical　variable,　the　ordinal　information　among　the　attribute　levels　can　be　exploited.　This　enables　us　to　simultaneously　describe　and　monitor　continuous　and　categorical　data　in　a　unified　framework　of　standardized　ranks,　based　on　which　a　multivariate　exponentially　weighted　moving　average　control　chart　is　proposed.　This　control　chart　specializes　in　detecting　location　shifts　in　continuous　data　and　in　latent　continuous　distributions　of　categorical　data.　Numerical　simulations　show　that　our　proposed　chart　can　efficiently　detect　location　shifts　and　is　robust　to　various　distributions.