Despite the great promise of service robots in everyday tasks, many roboethics issues remain to be addressed before these robots can physically work in human environments. Robot safety is one of the essential concerns for roboethics which is not just a design-time issue. It is also crucial to devise the required onboard monitoring and control strategies to enable robots to be aware of and react to anomalies (i.e., unexpected deviations from intended outcomes) that arise during their operations in the real world. The detection and identification of these anomalies is an essential first step toward fulfilling these requirements. Although several architectures are proposed for anomaly detection, identification is not yet thoroughly investigated. This task is challenging since indicators may appear long before anomalies are detected. In this paper, we propose a ConvoLUtional threE-stream Anomaly Identification (CLUE-AI) framework to address this problem. The framework fuses visual, auditory and proprioceptive data streams to identify everyday object manipulation anomalies. A stream of 2D images gathered through an RGB-D camera placed on the head of the robot is processed within a self-attention-enabled visual stage to capture visual anomaly indicators. The auditory modality provided by the microphone placed on the robot’s lower torso is processed within a designed convolutional neural network (CNN) in the auditory stage. Last, the force applied by the gripper and the gripper state is processed within a CNN to obtain proprioceptive features. These outputs are then combined with a late fusion scheme. Our novel three-stream framework design is analyzed on everyday object manipulation tasks with a Baxter humanoid robot in a semi-structured setting. The results indicate that CLUE-AI achieves an f-score of 94%, outperforming the other baselines in classifying anomalies.