To safely deploy legged robots in the real world, it is necessary to provide them with the ability to reliably detect unexpected contacts and accurately estimate the corresponding contact force. Therefore, a collision detection, isolation, and identification pipeline is proposed for a quadrupedal manipulator. An approach based on band-pass filtered forces is presented which accurately detects a collision, and estimates the collision time span. Next, the colliding body link is isolated. Finally, a collision identification method accurately identifies the magnitude and direction of the force. It is robust against model inaccuracies, unmodeled loads and any other potential source of disturbances acting on the robot. The framework is validated using extensive hardware experiments in various scenarios summing up to 416 collisions, including trotting and additional unmodeled load on the robot.