This report details quality control analysis of a multivariate dataset with 209 dimensions and 552 observations. Principal component analysis was conducted using both the covariance and correlation matrices. For both methods, 4 principal components were selected that explained over 80% and 90% of variability, respectively. Univariate control charts were created for each principal component to identify out of control data points. Phase I analysis removed between 0-12 outlier points over 7 iterations for the covariance method, and 0-4 points over 3 iterations for the correlation method. The correlation method was determined to be better since it accounts for differences in variable scales. Individual x-bar and CUSUM charts were recommended for Phase II analysis to detect mean shifts.