This paper presents a supervised smoke detection method that uses local and global features. This framework integrates and extends notions of many previous works to generate a new comprehensive method. First chrominance detection is used to screen areas that are suspected to be smoke. For these areas, local features are then extracted. The features are among homogeneity of GLCM and energy of wavelet. Then, global feature of motion of the smoke-color areas are extracted using a space-time analysis scheme. Finally these features are used to train an artificial intelligent. Here we use neural network, experiment compares importance of each feature. Hence, we can really know which features among those used by many previous works are really useful. The proposed method outperforms many of the current methods in the sense of correctness, and it does so in a reasonable computation time. It even has less limitation than conventional smoke sensors when used in open space. Best method for the experimental results is to use all the mentioned features as expected, to insure which is the best experiment result can be achieved. The achieved with high accuracy of result expected output is high value of true positive and low value of false positive. And show that our algorithm has good robustness for smoke detection.