We present an intelligent approach to multitrack dynamic range compression where all parameters are configured automatically based on side-chain feature extraction from the input signals. A method of adjustment experiment to explore how audio engineers set the ratio and threshold is described. We use multiple linear regression to model the relationship between different features and the experimental results. Parameter automations incorporate control assumptions based on this experiment and those derived from mixing literature and analysis. Subjective evaluation of the intelligent system is provided in the form of a multiple stimulus listening test where the system is compared against a no-compression mix, two human mixes, and an alternative approach. Results showed that mixes devised by our system are able to compete with or outperform manual mixes by semi-professionals under a variety of subjective criteria.