The paper deals with multivariate statistical methods used for failure prognostics in industrial processes. Modern on-line process monitoring system should support classic fault detection, isolation and diagnosis (FDI) sub-systems to avoid process down-time, increase production, optimize parameters of the production line, etc. However faults usually demand immediate intervention by operator, therefore by using reliable prognostic system, risks can be avoided, maintenance intervals can be scheduled, operation and production strategy can be updated, etc. Presented methods are intended for operator’s visual detection of process deviation (along with automated FDI systems) while process monitoring, diagnosis and data analysis tasks are running. By understanding nominal process operation, a hardly detectable small faults and drifts can be used to predict failure scenarios in process prognostics.