In population-based cancer studies, it is often of interest to compare cancer survival between different populations. However, in such studies the exact causes of death are often unavailable or unreliable. Net survival methods were developed to overcome this difficulty. Net survival is the survival that would be observed, in a hypothetical world, if the studied disease were the only possible cause of death. The Pohar-Perme estimator (PPE) is a non-parametric consistent estimator of net survival. In this paper, we present a log-rank-type test for comparing net survival functions estimated by this estimator between several groups. We expressed our test in the counting process framework to introduce the inverse probability weighting procedure as done in the PPE. We built a stratified version to control for categorical covariates affecting the outcome. Simulation studies were performed to evaluate the performance of our test and an application on real data is provided