A facility condition monitoring device for diagnosing an abnormality of a monitored target facility including a rotating machine or a reciprocating machine, based on state-quantity fluctuation data (D) representative of a time-dependent change of a state quantity measured on the monitored target facility and containing a set of measurement data of said state quantity, whereby each individual measurement data is associated with time data such as measurement time or time sequence, the facility condition monitoring device comprising: an abnormality-degree-calculation-model construction part configured to construct an abnormality degree calculation model (M) for calculating an abnormality degree (Q) of a monitored feature group (Ft) consisting of a plurality of features (F) extracted from the state-quantity fluctuation data (D) of the monitored target facility in a monitoring state, based on a normal feature group (Fe) consisting of a plurality of features (F) extracted from the state-quantity fluctuation data (D) of the monitored target facility in a normal state; an abnormality degree calculation part configured to calculate the abnormality degree (Q) of the monitoring feature group (Ft) with the abnormality degree calculation model (M); an abnormality determination part configured to determine whether an abnormality is present in the monitored target facility , based on the abnormality degree (Q); an abnormality-contribution-rate calculation part configured to obtain a contribution rate (C) of each of the plurality of features (F) constituting the monitored feature group (Ft) used for calculating the abnormality degree (Q) if it is determined by the abnormality determination part that an abnormality is present; and an abnormality cause identification part configured to identify an abnormality cause of the monitored target facility , based on the contribution rate (C) and a cause-and-effect matrix (T) which represents a relationship between the abnormality cause and the plurality of features (F). an abnormality-degree-calculation-model construction part configured to construct an abnormality degree calculation model (M) for calculating an abnormality degree (Q) of a monitored feature group (Ft) consisting of a plurality of features (F) extracted from the state-quantity fluctuation data (D) of the monitored target facility in a monitoring state, based on a normal feature group (Fe) consisting of a plurality of features (F) extracted from the state-quantity fluctuation data (D) of the monitored target facility in a normal state; an abnormality degree calculation part configured to calculate the abnormality degree (Q) of the monitoring feature group (Ft) with the abnormality degree calculation model (M); an abnormality determination part configured to determine whether an abnormality is present in the monitored target facility , based on the abnormality degree (Q); an abnormality-contribution-rate calculation part configured to obtain a contribution rate (C) of each of the plurality of features (F) constituting the monitored feature group (Ft) used for calculating the abnormality degree (Q) if it is determined by the abnormality determination part that an abnormality is present; and an abnormality cause identification part configured to identify an abnormality cause of the monitored target facility , based on the contribution rate (C) and a cause-and-effect matrix (T) which represents a relationship between the abnormality cause and the plurality of features (F). <EOS>