If your numpy installation uses system-wide BLAS libraries (this will most likely be the case unless you installed it through prebuilt windows binaries), you can retrieve this information at compile time to link python modules to BLAS. The function get_info in numpy.distutils.system_info will return a dictionary that contains the needed information to link against BLAS or an empty dict if no system-wide BLAS could be found. For example, MacOSX ships with it's own optimized BLAS routines, and get_info correctly reports that: [cc lang="python"] In [1]: from numpy.distutils.system_info import get_info In [2]: get_info('blas_opt') Out[2]: {'define_macros': [('NO_ATLAS_INFO', 3)], 'extra_compile_args': ['-msse3', '-I/System/Library/Frameworks/vecLib.framework/Headers'], 'extra_link_args': ['-Wl,-framework', '-Wl,Accelerate']} [/cc] The following example shows a setup.py that links against system-wide BLAS if possible. If no appropriate BLAS routine could be found, it will print a warning message, but will compile it's own BLAS routine and embed it in the python extension. [cc lang="python"] from os.path import join def configuration(parent_package='', top_path=None): import warnings from numpy.distutils.misc_util import Configuration from numpy.distutils.system_info import get_info, BlasNotFoundError config = Configuration('foo', parent_package, top_path) libfoo_files = ['foo.c'] blas_sources = [join('blas', 'daxpy.c'), join('blas', 'dscal.c')] blas_info = get_info('blas_opt') if not blas_info: warnings.warn(BlasNotFoundError.__doc__) libfoo_files.append(blas_sources) libraries = blas_info.pop('libraries', []) include_dirs = blas_info.pop('include_dirs', []) config.add_extension('foo', sources=sources, libraries=libraries, include_dirs=include_dirs, **blas_info ) return config if __name__ == '__main__': from numpy.distutils.core import setup setup(**configuration(top_path='').todict()) [/cc] A real-word example of this can be found in scipy.odr module and in scikits.learn's liblinear bindings.