The　monitoring　and　diagnosis　of　multivariate　categorical　processes　(MCPs)　have　drawn　increasing　attention　lately,　as　categorical　variables　have　been　frequently　involved　in　modern　quality　control　applications.　In　these　applications,　there　may　exist　causal　relationships　among　multiple　categorical　variables,　where　the　attribute　level　of　a　cause　variable　influences　that　of　its　effect　variable.　In　such　a　case,　shifts　occurring　in　a　cause　variable　will　propagate　to　its　effect　variable　based　on　the　causal　structure.　Furthermore,　there　usually　exists　natural　order　among　the　attribute　levels　of　some　categorical　variables　such　as　good,　neutral,　and　bad　for　measuring　the　product　quality.　By　assuming　a　latent　continuous　variable,　the　attribute　levels　of　an　ordinal　categorical　variable　can　be　determined　by　classifying　the　value　of　the　latent　variable　based　on　thresholds.　In　this　paper,　we　leverage　Bayesian　networks　(BNs)　to　characterize　MCPs　with　a　causal　structure,　where　the　categorical　variables　can　be　either　nominal,　ordinal　or　a　combination　of　both.　We　develop　one　general　control　chart　and　one　directional　control　chart,　both　of　which　fully　exploit　the　causal　relationships　and　the　ordinal　information　for　better　process　monitoring　and　diagnosis.　Numerical　simulations　have　demonstrated　the　superiority　and　robustness　of　our　method　in　detecting　and　diagnosing　the　conditional　probability　shifts　of　nominal　factors　as　well　as　the　conditional　latent　location　shifts　of　ordinal　factors.　IEEE