A novel analytical algorithm called Principal Discriminative Component Analysis (PDCA) is proposed to implement just-in-time feature extraction, so that the deviation between the online monitored data and the normal operating dataset can be timely uncovered. Instead of extracting representative signature inherited in the dataset given from the normal operating condition, the PDCA algorithm can always force the extracted latent features to be extremely discriminative to the inconsistency between the online sampled data and the given dataset. Therefore, the application of the PDCA algorithm in Multi-variate Statistical Process Monitoring (MSPM) can consistently guarantee its salient efficiency in contrast to the counterparts. The superiority and effectiveness of the PDCA-based MSPM approach are demonstrated through comparisons in monitoring both static and dynamic processes.