The compression index is used to estimate the consolidation settlement of clay-bearing soils. As the determination of compression index from oedometer tests is relatively time-consuming, empirical equations based on index properties can be useful. In this study the performance of widely used single and multi-variable empirical equations was evaluated using a database consisting of 135 test data. New empirical equations were developed utilizing least square regression analysis. In addition, an artificial neural network (ANN) with eight input variables was also developed to estimate the compression index. It was concluded that ANN provides the best results.