It has been widely observed that there exists a fundamental trade-off 
between the minimum distance properties and the iterative decoding
convergence behavior of turbo-like codes. While capacity achieving code
ensembles typically are asymptotically bad in the sense that their
minimum distance does not grow linearly with block length, and they
therefore exhibit an error floor at moderate-to-high signal to noise ratios,
asymptotically good codes usually converge further away from channel
capacity. We introduce the concept of tuned turbo codes, a family of
asymptotically good hybrid concatenated code ensembles, where minimum 
distance growth rates, convergence thresholds, and code rates can be traded-off 
using two tuning parameters, $\lambda$ and $\mu$.  By decreasing $\lambda$, 
the asymptotic minimum distance growth rate is reduced for the sake of 
improved iterative decoding convergence behavior, while increasing $\lambda$ 
raises the growth rate at the expense of worse convergence behavior, and 
thus the code performance can be tuned to fit the desired application.
By decreasing $\mu$, a similar tuning behavior can be achieved for
higher rate code ensembles.