Complex interactions and often dynamic variations in the process parameters and state variables are known to influence material removal rate (MRR) in the chemical mechanical planarization (CMP) of SiO2 film deposited on a Si wafer. Experimental investigations reported in this paper support the nonlinear stochastic characteristics of the CMP process dynamics. Prior modeling and monitoring efforts have attributed much of the complex patterns of the signals from the CMP process to extraneous noise. Consequently, these models have somewhat limited predictability. Sensor features that quantify the nonlinear stochastic dynamics of the CMP process are found to be effective surrogates to track variations in certain process parameters. A monitoring approach based on combining these nonlinear dynamic features with conventional statistical descriptors of sensor signals as well as process parameter settings is found to improve the tracking of MRR—which is one of the most important performance variables in the CMP process—by over 20% (linear R 2>80%) compared to the use of conventional features in a linear regression setting. The results consistently hold for a variety of process conditions tested using a battery of designed experiments.