The tire cornering stiffness plays a vital role in the functionality of vehicle dynamics control systems, particularly when it comes to stability and path tracking controllers. This parameter relies on various external variables such as the tire/ambient temperature, tire wear condition, the road surface state, etc. Ensuring a reliable estimation of the cornering stiffness value is crucial for control systems. This ensures that these systems can accurately compute actuator requests in a wide range of driving conditions. In this paper, a novel estimation method is introduced that relies solely on standard vehicle sensor data, including data such as steering wheel angles, longitudinal acceleration, lateral acceleration, yaw rate, and vehicle speed, among others. Initially, the vehicle's handling characteristics are deduced by estimating the understeer gradient. Subsequently, real-time estimates of the cornering stiffness values are derived by adapting the previously obtained parameters, all based on readily available vehicle sensor data. To enhance the robustness of the cornering stiffness model, a model for estimating vehicle mass is employed. The validity of the estimation method is confirmed through testing involving both low and high G excitation maneuvers, as well as real-world driving data on public roads, encompassing different road surface driving conditions. Additionally, the approach's robustness is assessed across various tire types and degrees of tire wear. Finally, the estimates of tire cornering stiffness are applied within a model to ascertain the classification of the road friction state.