Functional MRI is widely used to address basic and clinical neuroscience questions. In the key domains of fMRI experiments, i.e. acquisition, processing and analysis, and interpretation of data, developments are ongoing. The main issues are sensitivity for changes in fMRI signal that are associated with brain function, and the design of tasks with which brain functions are invoked. In this paper we address these issues, in terms of strengths, weaknesses and future developments. Acquisition of data is commonly achieved with techniques that measure blood oxygen level-dependent (BOLD) signal changes. Although the mechanisms that affect BOLD signal are complex and not well understood, fMRI yields results that agree with known functional topography. Sensitivity for task-related brain activity is expected to benefit from technological advances in acquisition, i.e. SENSE or parallel imaging, and higher field scanners (3 T). Data analysis is geared towards modelling sources of signal variation, i.e. reducing noise in the data time-series, and the cerebrovascular response to task-related changes in neuronal activity. Analytical algorithms such as connectivity and component analysis contribute to the extraction of meaningful information from fMRI datasets. The choice of tasks, and consequently of the statistical evaluation procedures, is best guided by the specific questions that are formulated a priori. The future is expected to bring more sophisticated questions, and tasks that allow for accurate modelling of involved brain functions. An example of a hypothesis-driven experiment is presented, where we investigated whether practise of a working memory task caused a shift in the neuronal representation of working memory or not.