A pragmatic method for selecting components and devices from a database or parameterized models is developed in this thesis. The quantitative framework presented here is sufficiently general to accommodate an entire device assembly, a component, or a sub-assembly. The details pertaining to a device or a component are classified into three sets of variables: (i) user-specifications, s (ii) device parameters, p , and (iii) device characteristics, c . Functional, practical, and performance-related attributes that a user can provide comprise user-specifications. Since, most often, a specification cannot be specified as a single number, we allow the user to enter a range with lower and upper bounds. Device parameters comprise the geometry and material properties, and device characteristics include functional requirements and performance criteria. Thus, for a device, all its functional and utility attributes are contained in the union of sets s and c , whereas the geometry and the material properties are in set p . The equations governing the physical behavior of the device are written in terms of s , p , and c . These equations may sometimes be readily available; when they are not, it may be necessary to formulate them as required. By solving the governing equations along with the inequalities that arise from the lower and upper bounds on s , we obtain feasible ranges on p and c . Then, for any pair of device characteristics, a 2D feasible map is drawn, to visually portray the consequences of user-specifications. If the feasible map is null, small, or large, it indicates that the user-specifications are infeasible, stringent, or there is much scope for design, respectively. This can be inferred even before the designs are considered. Juxtaposed on the feasible map are points or lozenges corresponding to the quantitative attributes of the entries in the database. The ones that lie inside the feasible map can be reckoned as meeting the user-specifications and thus, enabling selection. On the other hand, if there is no database or none of the devices in the database lie inside the feasible map, we can identify the feasible ranges of all the design parameters for every point inside the feasible map. This information is useful to the designer to redesign and arrive at feasible designs by using parameterized models of the device. A Graphical User Interface (GUI) is developed to facilitate the user-interaction. The utility of the selection framework is demonstrated with a variety of case-studies including miniature pumps, heat pulse-based soil-moisture sensors, springs, flywheels, compliant mechanisms, micromechanical suspensions, etc. The latter two use kineto-elastic characteristics of deformable components. The framework, when used for materials selection, can be seen as an extension of Ashby’s materials selection method. This is also illustrated with two examples.