Segregation in hot-mix asphalt pavement is a common failure during the construction process. The prevailing segregation detection methods can be used to detect and evaluate segregation only after segregation occurs. This study proposes a real time segregation detection method by using machine learning classifier to categorize the images of the paved mixture (IPM) during construction. The study first manually labeled 224 various levels of hot mix asphalt segregation images. Then, 14 texture features such as contrast, correlation of the IPM were calculated by the gray level co-occurrence matrix (GLCM). Next, the principal component analysis (PCA) was done to reduce the 14 features to 6 main components. Later on, the 6 main components were fed to a Naive Bayesian classifier to categorize the segregation level. Finally, the classification results indicate that the Naïve Bayesian classifier has 80% accuracy when compared with the manually labelled results. Results of this study can potentially be adapted for real-time and large-scale hot mix asphalt segregation evaluation.