Fault detection in multi-rate process systems is a challenging task. To make such process plant available till the next shutdown it is essential to detect faults and take corrective measures before they lead to failure. Common techniques used for fault detection of multi-rate systems include threshold-based detectors, statistical detectors, and machine learning-based detectors, one such statistical detector technique is Multiple Probabilistic Principal Component Analysis (MPPCA). MPPCA uses probabilistic PCA to detect fault signals from multiple sensors without down-sampling or up-sampling It considers the inter-dependencies among the sensors and helps in detecting and diagnosing faults in a timely and accurate manner, in this work MPPCA is applied for fault detection of Two-Phase Reactor-Condenser system with Recycle (TPRCR) in which three different classes of measurement are considered. Temperature and pressure measurement are considered as fast rate measurements, Liquid holdups are considered as medium rate and mole fractions are available at slower rates. This measurement data is used for development of MPPCA model using Expectation-Maximization (EM) algorithm. Based on this T² and SPE statistics are developed which are used for Fault-detection in TPRCR system and efficacy of MPPCA method is found to be satisfactory.