Abstract: Background and Objective: Existing work on human activity recognition mainly focuses on recognizing activities for a single resident. However, in real life, activities are often performed by multiple users. This study aimed to recognize multiple resident activities inside home using deep neural networks and an ontological approach for features selection. Materials and Methods: This model comprised an ontological approach method for robust features extraction and selection, a Deep Belief Network (DBN) algorithm for recognising three categories of multiple resident activities inside home. A simulated experiment was conducted using publicly two multiple resident CASAS databases collected at Washington State University (WSU) and the proposed approach was compared with traditional recognition approaches such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results: The results showed that the proposed approach based on DBN and ontology produce better accuracy results compared to SVM and ANN. Conclusion: In this research, deep neural network algorithm had been successfully developed to recognize daily life human activities using features manually extracted.