The unprecedented growth of machine-readable data throughout modern industrial systems has major repercussions for process monitoring activities. In contrast to model-based process monitoring that requires the physical and mathematical knowledge of the system in advance, the data-driven schemes provide an efficient alternative to extract and analyze process information directly from recorded process data. This paper introduces the least squares sparse principal component analysis to obtain readily interpretable sparse principal components. This is done in the context of parallel coordinates, which facilitate the visualization of high dimensional data. The key contribution is the establishment of control limits on independent sparse principal component and residual spaces to facilitate fault detection, complemented by the use of the Random Forests algorithm to carry out the fault diagnosis step. The proposed method is applied to the Tennessee Eastman process to highlight its merits.