New metrological techniques have been developed for mechanical characterization at the microscale and nanoscale as follows: (1) Development of a control system and integrated imaging capability at the microscale and nanoscale for a new biaxial microtensile tester, (2) a new method for characterizing nonlinearity in AFM imaging using Digital Image Correlation (DIC), and (3) development of pointwise DIC technique. In the biaxial microtensile tester, loading of specimen is induced through the opposing motion of dual picomotor linear actuators in orthogonal directions with a displacement resolution of less than 30 nm. Using an optical microscope, in situ digital images are obtained and analyzed with DIC to determine the full field displacements at the microscale over an Area of Interest (AOI) in order to characterize the biaxial performance of the microtensile tester. An objective AFM has been integrated into the biaxial microtensile tester to obtain in situ digital images of topographic microstructural features at the nanoscale. These topographic images can then be converted to gray scale images with textures that are suitable for DIC to calculate full field displacements at the nanoscale. This measurement capability is demonstrated on a sputtered nanocrystalline copper film subjected to uniaxial loading in the microtensile tester. Since image quality is critical to the accuracy of the nanoscale DIC measurements, a new method was developed to calibrate the errors induced by the nonlinearity of AFM scanning. In this new method, the DIC technique was applied to AFM images of sputtered nanocrystalline NiTi films to calculate the displacement errors caused by the probe offset that must be eliminated from the apparent displacement field. The conventional DIC technique assumes a zero-order or first order approximation of the variation in displacement fields (i.e., displacement gradients) relative to the center of a subset of the image. In the case of displacement fields associated with the microstructure of a material, the displacement gradients can vary discontinuously, which violates the assumed nature of the displacement gradients in the conventional DIC. Therefore, a pointwise DIC technique has been developed to calculate displacements independently at each pixel location, eliminating the constraints imposed by the subset on the calculated displacements. Because of the potentially large number of unknown displacement variables that need to be determined using this approach, an efficient Genetic Algorithm (GA) optimization algorithm with a Differential Evolution (DE) method was investigated for optimizing the correlation function. To guarantee uniqueness of the optimized displacement field, the correlation function was modified using intensity gradients that had to be transformed from an Eulerian to Lagrangian reference frame using displacement gradients. The theoretical development of pointwise DIC is discussed in detail using ideal sinusoidal images, and its validation using real images is also presented.