A virtual environment is a set of surroundings that appears to exist to a user through sensory stimuli provided by a computer. By virtual environment, we mean to include environments supporting the full range from VR to pure reality. A necessity for virtual environments is knowledge of the location of objects in the environment. This is referred to as the tracking problem, which points to the need for accurate and precise tracking in virtual environments. Marker-based tracking is a technique which employs fiduciary marks to determine the pose of a tracked object. A collection of markers arranged in a rigid configuration is called a tracking probe. The performance of marker-based tracking systems depends upon the fidelity of the pose estimates provided by tracking probes. The realization that tracking performance is linked to probe performance necessitates investigation into the design of tracking probes for proponents of marker-based tracking. The challenges involved with probe design include prediction of the accuracy and precision of a tracking probe, the creation of arbitrarily-shaped tracking probes, and the assessment of the newly created probes. To address these issues, we present a pioneer framework for designing conformal tracking probes. Conformal in this work means to adapt to the shape of the tracked objects and to the environmental constraints. As part of the framework, the accuracy in position and orientation of a given probe may be predicted given the system noise. The framework is a methodology for designing tracking probes based upon performance goals and environmental constraints. After presenting the conformal tracking framework, the elements used for completing the steps of the framework are discussed. We start with the application of optimization methods for determining the probe geometry. Two overall methods for mapping markers on tracking probes are presented, the Intermediary Algorithm and the Viewpoints Algorithm. Next, we examine the method used for pose estimation and present a mathematical model of error propagation used for predicting probe performance in pose estimation. The model uses a first-order error propagation, perturbing the simulated marker locations with Gaussian noise. The marker locations with error are then traced through the pose estimation process and the effects of the noise are analyzed. Moreover, the effects of changing the probe size or the number of markers are discussed. Finally, the conformal tracking framework is validated experimentally. The assessment methods are divided into simulation and post-fabrication methods. Under simulation, we discuss testing of the performance of each probe design. Then, post-fabrication assessment is performed, including accuracy measurements in orientation and position. The framework is validated with four tracking probes. The first probe is a six-marker planar probe. The predicted accuracy of the probe was 0.06 deg and the measured accuracy was 0.083 plus/minus 0.015 deg. The second probe was a pair of concentric, planar tracking probes mounted together. The smaller probe had a predicted accuracy of 0.206 deg and a measured accuracy of 0.282 plus/minus 0.03 deg. The larger probe had a predicted accuracy of 0.039 deg and a measured accuracy of 0.017 plus/minus 0.02 deg. The third tracking probe was a semi-spherical head tracking probe. The predicted accuracy in orientation and position was 0.54 plus/minus 0.24 deg and 0.24 plus/minus 0.1 mm, respectively. The experimental accuracy in orientation and position was 0.60 plus/minus 0.03 deg and 0.225 plus/minus 0.05 mm, respectively. The last probe was an integrated, head-mounted display probe, created using the conformal design process. The predicted accuracy of this probe was 0.032 plus/minus 0.02 degrees in orientation and 0.14 plus/minus 0.08 mm in position. The measured accuracy of the probe was 0.028 plus/minus 0.01 degrees in orientation and 0.11 plus/minus 0.01 mm in position. These results constitute an order of magnitude improvement over current marker-based tracking probes in orientation, indicating the benefits of a conformal tracking approach. Also, this result translates to a predicted positional overlay error of a virtual object presented at 1m of less than 0.5 mm, which is well above reported overlay performance in virtual environments.