Propaganda is information or ideas that an organized group or government spreads to influence peopleś opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.