The use of sampled-data multidetected-output controllers for model reference adaptive control of linear systems with unknown parameters is investigated. Multidetected-output controllers contain a sampling mechanism in which the system output is detected many times over one period. ; Such a control allows us to assign an arbitrary discrete-time transfer function to the sampled closed-loop system and does not make any assumptions on the plant but controllability and observability. An indirect adaptive control scheme based on these sampled-data controllers is proposed, which estimates the controller parameters on-line. ; By using the proposed adaptive algorithm, the model reference adaptive control problem is reduced to the determination of a fictitious static state feedback controller, due to the merits of dynamic multidetected-output controllers. The known techniques usually resort to the direct computation of dynamic controllers. ; The controller determination reduces to the simple problem of solving a linear algebraic system of equations whereas in known techniques a matrix polynomial Diophantine equation is usually needed to be solved. Moreover, persistent excitation of the continuous-time plant is provided without making any special richness assumption on the reference signal.