Online sensing can provide useful information in monitoring applications, for example, machine health monitoring, structural condition monitoring, environmental monitoring, and many more. Missing data is generally a significant issue in the sensory data that is collected online by sensing systems, which may affect the goals of monitoring programs. In this paper, a sequence-to-sequence learning model based on a recurrent neural network (RNN) architecture is presented. In the proposed method, multivariate time series of the monitored parameters is embedded into the neural network through layer-by-layer encoders where the hidden features of the inputs are adaptively extracted. Afterwards, predictions of the missing data are generated by network decoders, which are one-step-ahead predictive data sequences of the monitored parameters. The prediction performance of the proposed model is validated based on a real-world sensory dataset. The experimental results demonstrate the performance of the proposed RNN-encoder-decoder model with its capability in sequence-to-sequence learning for online imputation of sensory data.