In this work, we have developed an efficient electronic nose (E-nose) for a smart home to detect and monitor various volatile organic compounds (VOCs) that are usually generated in the household sector during several daily chores (e.g., worship, safety & security, hygiene, LPG leakage detection, cooking and smoking). We have implemented the proposed electronic nose using a six-element low-cost MQ-series-based gas sensor array and aESP-32 Microcontroller. The raw gas sensor array responses are first transformed in the Standardised Principal Component Analysis (SPCA) based analysis domain. In this domain, the gas sensor data shows well shaped and separated clusters belonging to various VOCs. A simplistic classifier is then designed using Artificial Neural Network (ANN) trained in the SPCA transformed domain itself which outperforms in classification accuracy. In this experiment, we have captured 2465training samples belonging to 17 diverse VOCs (odors and smokes) commonly found in household ambience. Another 85 samples were also captured for testing purposes which were not used during the training of the ANN classifier. The accuracy in correct classification has been 96.47% for the 17-class test samples while the precision, recall and F1-score were 96%, 95% and 95%, respectively. The mean squared error (MSE) in this experiment was between 1.35 × 10-6 and 2.15 × 10-2with an average MSE of1.42 × 10-3 .