Industrial process monitoring is a significant task and has started to get better solved by deep learning. However, ensuring that the learned deep features from process data are effective and interpretable for the monitoring task remains a challenge. In this article, a slow feature analysis-aided autoencoder (SFA-AE) is proposed for interpretable process monitoring. The SFA-AE, which combines the advantages of SFA and an autoencoder (AE), enables the learning of deep slow variation patterns from the high-level features extracted by the AE. Particularly, the AE additionally incorporates in convolutional long short-term memories for representing the spatiotemporal relationships of multivariate time-series process data. These slow patterns that are able to heavily highlight abnormal states are used to construct monitoring statistics and then improve the reliability of fault detection. Most importantly, variable relative attention and its control limit based on self-attention mechanisms are developed for fault diagnosis. The proposed self-attention emphasizes the different contributions of process variables to fault detection, making it possible to interpret fault variables in an end-to-end way. In the experiments on the Tennessee Eastman process, the proposed process monitoring method obtains superior fault detection and diagnosis performance than state-of-the-art ones.