It is observed that digital versions of globally stable adaptive stabilization algorithms are, at best, locally stable due to incompatibilities between the gain adaptation algorithms and the choice of sampling rate. This fact suggests the problem of retrieving global asymptotic stability for such difficulties by modifications to the stabilization algorithms to include sampling rates as control variables and the inclusion of extra plant information to relate sampling rates to gain evolutions. In this contribution, the theoretical problem of adaptive stabilization of single-input single-output (SISO) linear systems S(A, B, C) in R(n) is approached using the sampling rate as the basic adaptive mechanism. A wide range of sample interval adaptation schemes is derived that guarantees global asymptotic stability of the sampled outputs from the plant for any minimum-phase SISO systems satisfying the relative degree one constraint CB not equal 0. The control laws are ’universal’ in the sense that stabilization is achieved despite a lack of knowledge of the systems parameters and state dimension. They also guarantee the stability of the underlying continuous system’s state response if either the system has no open-loop oscillatory unstable poles or it is under a sufficiently fast initial choice of sampling interval, or for any other generically chosen sample interval.