The presence of atmospheric hazards in confined space can contribute towards atmospheric hazards accidents that threaten the worker safety and industry progress. To avoid this, the environment needs to be observed. The air sample can be monitored using the integration of electronic nose (e-nose) and mobile robot. Current technology to monitor the atmospheric hazards is applied before entering confined spaces called pre-entry by using a gas detector. This work aims to develop an instrument to assist workers during pre-entry for atmosphere testing. The developed instrument using specific sensor arrays which were identified based on main hazardous gasses effective value. The instrument utilizes multivariate statistical analysis that is Principal Component Analysis (PCA) for discriminate the different concentrations of gases. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) that is Radial Basis Function Neural Network (RBFNN) are used to classify the acquired data from the air sample. This will increase the instrument capability while the portability will minimize the size and operational complexity as well as increase user friendliness. The instrument was successfully developed, tested and calibrated using fixed concentrations of gases samples. The results proved that the developed instrument is able to discriminate an air sample using PCA with total variation for 99.42%, while the classifier success rate for SVM and RBFNN indicates at 99.28% for train performance and 98.33% for test performance. This will contribute significantly to acquiring a new and alternative method of using the instrument for monitoring the atmospheric hazards in confined space.