Morning Star Track is a 3.4 km Medium 4WD trail in Blackwood, Victoria, Australia. Average drive time is 23.91 minutes.
If you're seeking a well-rounded medium-difficulty drive that doesn't demand extreme technical skill, the Morning Star Track near Blackwood delivers excellent variety in a compact 3.4 km package. You'll navigate a mix of rocky outcrops, tree-lined sections, and open forest terrain that keeps your attention without overwhelming your vehicle. The absence of steep grades makes this trail particularly accessible for drivers building their off-road confidence or those operating vehicles with modest ground clearance.
What sets Morning Star apart is its engaging character—you'll encounter enough rocky patches and uneven surfaces to test your line-finding abilities, while the predominantly moderate conditions let you focus on reading terrain and refining your driving technique. The scenic surroundings and varied landscape mean you're rarely bored during the 24-minute run, and the relatively short distance makes it perfect for a focused practice session or a quick adventure between other activities.
Before heading out, ensure your vehicle is in good mechanical condition and carry basic recovery gear. The trail's popularity among the 4WD community—with 36 vehicles already logged through Newtracs—confirms it's both achievable and rewarding. You should check current conditions before departure and remember that even moderate trails demand respect for the environment and attention to changing weather. This is ideal terrain for honing your skills while enjoying Victoria's forest country.
Explore Morning Star Track in the app
Offline maps, live conditions & more
3.4 km
Distance
24 min
Avg Time
14 km/h
Avg Speed
--
Steep Grade
See how many vehicles have driven this trail
Already have an account?
See who's driving this trail and when
Already have an account?
11°C
Clear
4 km/h NNW
82%
11°C
0
7-Day Forecast
Sunny
Partly Cloudy
Sunny
Sunny
Sunny
Patchy rain nearby
Patchy rain nearby
Get offline topo maps, live conditions, and data-driven difficulty ratings.