This study presents the development of soft sensors based on just-in-time learning (JITL) and dynamic time warping (DTW) for online quality prediction in multi-grade processes. Most industrial chemical processes are multi-grade processes that produce multiple products with distinct properties. Multi-grade processes, however, are difficult to monitor and control due to frequent process transitions and abrupt changes in operating conditions. The DTWbased JITL soft sensor modeling approach is proposed as a solution to the complexity of multi-grade process modeling. In the JITL modeling approach, a local model is trained online using historical samples that are similar to the query sample, allowing the model to account for multi-grade characteristics and process drifts. To account for process dynamics and temporal correlations, the suggested approach utilizes a data sequence as an input rather than a single data point. DTW calculates the similarity of data sequences by stretching the sequences to determine an optimal warping path. Additionally, sensitivity analyses of model hyperparameters are performed and a cross-correlation-based hyperparameter optimization approach is proposed. The advantages of the proposed approach are verified via multi-grade simulation studies. As a result, the proposed model outperforms a conventional JITL model based on the Euclidean distance.